The document discusses representing relational spatiotemporal data using information granules. It proposes:
1) Describing the relational data using a vocabulary of granular descriptors formed from Cartesian products of spatial, temporal, and signal information granules. This granular representation provides an interpretable perspective on the data.
2) Analyzing the capabilities of different vocabularies to capture the essence of the data through the processes of granulation and degranulation, where the original data is reconstructed from its granular representation. The quality of reconstruction is used to optimize the vocabulary.
3) Extending the approach to analyze evolvability of the granular description as the relational data changes across consecutive
Drsp dimension reduction for similarity matching and pruning of time series ...IJDKP
Ā
The document summarizes a research paper that proposes a framework called DRSP (Dimension Reduction for Similarity Matching and Pruning) for time series data streams. DRSP addresses the challenges of large streaming data size by:
1) Performing dimension reduction using a Multi-level Segment Mean technique to compactly represent the data while retaining crucial information.
2) Incorporating a similarity matching technique to analyze if new data objects match existing streams.
3) Applying a pruning technique to filter out non-relevant data object pairs and join only relevant pairs.
The framework aims to reduce storage and computation costs for similarity matching on large time series data streams.
This document provides an overview of techniques for temporal data mining. It discusses how temporal sequences arise in domains like engineering, science, finance, and healthcare. It covers approaches for representing temporal sequences, including keeping data in its original form, piecewise approximations, transformations to other domains, discretization, and generative models. It also discusses measuring similarity between sequences and techniques for classification and relation finding in temporal data, including frequent pattern detection and prediction. The goal is to classify and organize temporal data mining techniques to help practitioners address problems involving temporal data.
SCAF ā AN EFFECTIVE APPROACH TO CLASSIFY SUBSPACE CLUSTERING ALGORITHMSijdkp
Ā
Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high
dimensional data. Many significant subspace clustering algorithms exist, each having different
characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive
classification scheme is essential which will consider all such characteristics to divide subspace clustering
approaches in various families. The algorithms belonging to same family will satisfy common
characteristics. Such a categorization will help future developers to better understand the quality criteria to
be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In
this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithmsā Family).
Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc.
As an illustration, we further provided a comprehensive, systematic description and comparison of few
significant algorithms belonging to āAxis parallel, overlapping, density basedā SCAF.
This document describes three approaches for indexing moving point data over time: the 3D R-tree, the HR-tree, and the 2+3 R-tree. An experiment was conducted to evaluate the storage space requirements and query performance of each approach. The results showed that while the HR-tree required the most storage space, its query processing costs were over 50% lower than the 3D R-tree and 2+3 R-tree. Compared to maintaining separate R-trees for each time state, the HR-tree also offered similar query performance while using around one third less storage space.
A fuzzy clustering algorithm for high dimensional streaming dataAlexander Decker
Ā
This document summarizes a research paper that proposes a new dimension-reduced weighted fuzzy clustering algorithm (sWFCM-HD) for high-dimensional streaming data. The algorithm can cluster datasets that have both high dimensionality and a streaming (continuously arriving) nature. It combines previous work on clustering algorithms for streaming data and high-dimensional data. The paper introduces the algorithm and compares it experimentally to show improvements in memory usage and runtime over other approaches for these types of datasets.
The document proposes an extension to the M-tree family of index structures called M*-tree. M*-tree improves upon M-tree by maintaining a nearest-neighbor graph within each node. The nearest-neighbor graph stores, for each entry in a node, a reference and distance to its nearest neighbor among the other entries in that node. This additional structure allows for more efficient filtering of non-relevant subtrees during search queries through the use of "sacrifice pivots". The experiments showed that M*-tree can perform searches significantly faster than M-tree while keeping construction costs low.
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...Editor IJCATR
Ā
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
Juha vesanto esa alhoniemi 2000:clustering of the somArchiLab 7
Ā
This document summarizes a research paper that proposes clustering the self-organizing map (SOM) as a way to analyze cluster structure in data. The paper discusses:
1) Using a two-stage process where data is first mapped to prototypes using a SOM, then the SOM prototypes are clustered, reducing computational load compared to direct data clustering.
2) Different clustering algorithms that can be applied to the SOM prototypes, including hierarchical and partitive (k-means) methods.
3) The benefits of clustering the SOM include noise reduction and being able to cluster large datasets more efficiently.
Drsp dimension reduction for similarity matching and pruning of time series ...IJDKP
Ā
The document summarizes a research paper that proposes a framework called DRSP (Dimension Reduction for Similarity Matching and Pruning) for time series data streams. DRSP addresses the challenges of large streaming data size by:
1) Performing dimension reduction using a Multi-level Segment Mean technique to compactly represent the data while retaining crucial information.
2) Incorporating a similarity matching technique to analyze if new data objects match existing streams.
3) Applying a pruning technique to filter out non-relevant data object pairs and join only relevant pairs.
The framework aims to reduce storage and computation costs for similarity matching on large time series data streams.
This document provides an overview of techniques for temporal data mining. It discusses how temporal sequences arise in domains like engineering, science, finance, and healthcare. It covers approaches for representing temporal sequences, including keeping data in its original form, piecewise approximations, transformations to other domains, discretization, and generative models. It also discusses measuring similarity between sequences and techniques for classification and relation finding in temporal data, including frequent pattern detection and prediction. The goal is to classify and organize temporal data mining techniques to help practitioners address problems involving temporal data.
SCAF ā AN EFFECTIVE APPROACH TO CLASSIFY SUBSPACE CLUSTERING ALGORITHMSijdkp
Ā
Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high
dimensional data. Many significant subspace clustering algorithms exist, each having different
characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive
classification scheme is essential which will consider all such characteristics to divide subspace clustering
approaches in various families. The algorithms belonging to same family will satisfy common
characteristics. Such a categorization will help future developers to better understand the quality criteria to
be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In
this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithmsā Family).
Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc.
As an illustration, we further provided a comprehensive, systematic description and comparison of few
significant algorithms belonging to āAxis parallel, overlapping, density basedā SCAF.
This document describes three approaches for indexing moving point data over time: the 3D R-tree, the HR-tree, and the 2+3 R-tree. An experiment was conducted to evaluate the storage space requirements and query performance of each approach. The results showed that while the HR-tree required the most storage space, its query processing costs were over 50% lower than the 3D R-tree and 2+3 R-tree. Compared to maintaining separate R-trees for each time state, the HR-tree also offered similar query performance while using around one third less storage space.
A fuzzy clustering algorithm for high dimensional streaming dataAlexander Decker
Ā
This document summarizes a research paper that proposes a new dimension-reduced weighted fuzzy clustering algorithm (sWFCM-HD) for high-dimensional streaming data. The algorithm can cluster datasets that have both high dimensionality and a streaming (continuously arriving) nature. It combines previous work on clustering algorithms for streaming data and high-dimensional data. The paper introduces the algorithm and compares it experimentally to show improvements in memory usage and runtime over other approaches for these types of datasets.
The document proposes an extension to the M-tree family of index structures called M*-tree. M*-tree improves upon M-tree by maintaining a nearest-neighbor graph within each node. The nearest-neighbor graph stores, for each entry in a node, a reference and distance to its nearest neighbor among the other entries in that node. This additional structure allows for more efficient filtering of non-relevant subtrees during search queries through the use of "sacrifice pivots". The experiments showed that M*-tree can perform searches significantly faster than M-tree while keeping construction costs low.
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...Editor IJCATR
Ā
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
Juha vesanto esa alhoniemi 2000:clustering of the somArchiLab 7
Ā
This document summarizes a research paper that proposes clustering the self-organizing map (SOM) as a way to analyze cluster structure in data. The paper discusses:
1) Using a two-stage process where data is first mapped to prototypes using a SOM, then the SOM prototypes are clustered, reducing computational load compared to direct data clustering.
2) Different clustering algorithms that can be applied to the SOM prototypes, including hierarchical and partitive (k-means) methods.
3) The benefits of clustering the SOM include noise reduction and being able to cluster large datasets more efficiently.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
Ā
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
Distribution of maximal clique size underijfcstjournal
Ā
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
This document describes a context-aware automatic traffic notification system for cell phones that can learn a user's common destinations and routes over time using location and context data. It collects GPS and other data from users, identifies important locations through clustering, learns frequent routes between locations, and can predict a user's destination and route to then notify them of any traffic conditions. The system is implemented on a mobile phone to provide automated traffic alerts to users during their daily commutes without needing to manually enter a destination.
MULTIDIMENSIONAL ANALYSIS FOR QOS IN WIRELESS SENSOR NETWORKSijcses
Ā
Nodes in Mobile Ad-hoc network are connected wirelessly and the network is auto configuring [1]. This paper introduces the usefulness of data warehouse as an alternative to manage data collected by WSN.Wireless Sensor Network produces huge quantity of data that need to be proceeded and homogenised, so as to help researchers and other people interested in the information. Collected data is managed and compared with other coming from datasources and systems could participate in technical report and decision making. This paper proposes a model to design, extract, transform and normalize data collected by Wireless Sensor Networks by implementing a multidimensional warehouse for comparing many aspects in WSN such as (routing protocol[4], sensor, sensor mobility, cluster ā¦.). Hence, data warehouse defined and applied to the context above is presented as a useful approach that gives specialists row data and information for decision processes and navigate from one aspect to another.
The document proposes a strategy for clustering distributed databases using self-organizing maps (SOM) and K-means algorithms. The strategy applies SOM locally to each distributed data set to obtain representative subsets, then combines the results and applies SOM and K-means globally. Specifically, it performs local SOM clustering, sends representative data to a central site, applies SOM again on the combined data, then uses K-means on the unified map to produce the final clustering result.
Optimized Access Strategies for a Distributed Database DesignWaqas Tariq
Ā
Abstract Distributed Database Query Optimization has been an active area of research for Database research Community in this decade. Research work mostly involves mathematical programming and evolving new algorithm design techniques in order to minimize the combined cost of storing the database, processing transactions and communication amongst various sites of storage. The complete problem and most of its subsets as well are NP-Hard. Most of proposed solutions till date are based on use of Enumerative Techniques or using Heuristics. In this paper we have shown benefits of using innovative Genetic Algorithms (GA) for optimizing the sequence of sub-query operations over the enumerative methods and heuristics. A stochastic simulator has been designed and experimental results show encouraging improvements in decreasing the total cost of a query. An exhaustive enumerative method is also applied and solutions are compared with that of GA on various parameters of a Distributed Query, like up to 12 joins and 10 sites. Keywords: Distributed Query Optimization, Database Statistics, Query Execution Plan, Genetic Algorithms, Operation Allocation.
The document surveys previous work on clustering of time series data. It discusses three main approaches to time series clustering: (1) working directly with raw time series data, (2) extracting features from the raw data and clustering the features, and (3) building models from the raw data and clustering the model parameters. It also summarizes commonly used clustering algorithms, measures for determining time series similarity, and criteria for evaluating clustering results. The document categorizes and discusses past time series clustering research and identifies topics for future work. It aims to provide an overview of the field of time series clustering.
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
Ā
Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
Ā
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
Ā
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
Ā
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
This document provides an overview of several clustering algorithms. It begins by defining clustering and its importance in data mining. It then categorizes clustering algorithms into four main types: partitional, hierarchical, grid-based, and density-based. For each type, some representative algorithms are described briefly. The document also reviews several popular clustering algorithms like k-means, CLARA, PAM, CLARANS, and BIRCH in more detail. It discusses aspects like the algorithms' time complexity, types of data handled, ability to detect clusters of different shapes, required input parameters, and advantages/disadvantages. Overall, the document aims to guide selection of suitable clustering algorithms for specific applications by surveying their key characteristics.
Spatial data mining involves discovering patterns from large spatial datasets. It differs from traditional data mining due to properties of spatial data like spatial autocorrelation and heterogeneity. Key spatial data mining tasks include clustering, classification, trend analysis and association rule mining. Clustering algorithms like PAM and CLARA are useful for grouping spatial data objects. Trend analysis can identify global or local trends by analyzing attributes of spatially related objects. Future areas of research include spatial data mining in object oriented databases and using parallel processing to improve computational efficiency for large spatial datasets.
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
Ā
This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Ā
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
This document provides an overview of stream data mining techniques. It discusses how traditional data mining cannot be directly applied to data streams due to their continuous, rapid nature. The document outlines some essential methodologies for analyzing data streams, including sampling, load shedding, sketching, and data summarization techniques like reservoirs, histograms, and wavelets. It also discusses challenges in applying these techniques to data streams and open problems in the emerging field of stream data mining.
Clustering is also known as data segmentation aims to partitions data set into groups, clusters, according to their similarity. Cluster analysis has been extensively studied in many researches. There are many algorithms for different types of clustering. These classical algorithms can't be applied on big data due to its distinct features. It is a challenge to apply the traditional techniques on large unstructured data. This study proposes a hybrid model to cluster big data using the famous traditional K-means clustering algorithm. The proposed model consists of three phases namely; Mapper phase, Clustering Phase and Reduce phase. The first phase uses map-reduce algorithm to split big data into small datasets. Whereas, the second phase implements the traditional clustering K-means algorithm on each of the spitted small data sets. The last phase is responsible of producing the general clusters output of the complete data set. Two functions, Mode and Fuzzy Gaussian, have been implemented and compared at the last phase to determine the most suitable one. The experimental study used four benchmark big data sets; Covtype, Covtype-2, Poker, and Poker-2. The results proved the efficiency of the proposed model in clustering big data using the traditional K-means algorithm. Also, the experiments show that the Fuzzy Gaussian function produces more accurate results than the traditional Mode function.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
Ā
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
Windows 8 javascript apps ā getting it rightbrendankowitz
Ā
This document summarizes key points about developing Windows 8 JavaScript apps using WinJS. It discusses why to use WinJS, getting started, differences from web development, third party controls, testing, using C# libraries, asynchronous programming, storage options, debugging, and common gotchas. The document provides an overview of concepts and best practices for WinJS app development.
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
Ā
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
Distribution of maximal clique size underijfcstjournal
Ā
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
This document describes a context-aware automatic traffic notification system for cell phones that can learn a user's common destinations and routes over time using location and context data. It collects GPS and other data from users, identifies important locations through clustering, learns frequent routes between locations, and can predict a user's destination and route to then notify them of any traffic conditions. The system is implemented on a mobile phone to provide automated traffic alerts to users during their daily commutes without needing to manually enter a destination.
MULTIDIMENSIONAL ANALYSIS FOR QOS IN WIRELESS SENSOR NETWORKSijcses
Ā
Nodes in Mobile Ad-hoc network are connected wirelessly and the network is auto configuring [1]. This paper introduces the usefulness of data warehouse as an alternative to manage data collected by WSN.Wireless Sensor Network produces huge quantity of data that need to be proceeded and homogenised, so as to help researchers and other people interested in the information. Collected data is managed and compared with other coming from datasources and systems could participate in technical report and decision making. This paper proposes a model to design, extract, transform and normalize data collected by Wireless Sensor Networks by implementing a multidimensional warehouse for comparing many aspects in WSN such as (routing protocol[4], sensor, sensor mobility, cluster ā¦.). Hence, data warehouse defined and applied to the context above is presented as a useful approach that gives specialists row data and information for decision processes and navigate from one aspect to another.
The document proposes a strategy for clustering distributed databases using self-organizing maps (SOM) and K-means algorithms. The strategy applies SOM locally to each distributed data set to obtain representative subsets, then combines the results and applies SOM and K-means globally. Specifically, it performs local SOM clustering, sends representative data to a central site, applies SOM again on the combined data, then uses K-means on the unified map to produce the final clustering result.
Optimized Access Strategies for a Distributed Database DesignWaqas Tariq
Ā
Abstract Distributed Database Query Optimization has been an active area of research for Database research Community in this decade. Research work mostly involves mathematical programming and evolving new algorithm design techniques in order to minimize the combined cost of storing the database, processing transactions and communication amongst various sites of storage. The complete problem and most of its subsets as well are NP-Hard. Most of proposed solutions till date are based on use of Enumerative Techniques or using Heuristics. In this paper we have shown benefits of using innovative Genetic Algorithms (GA) for optimizing the sequence of sub-query operations over the enumerative methods and heuristics. A stochastic simulator has been designed and experimental results show encouraging improvements in decreasing the total cost of a query. An exhaustive enumerative method is also applied and solutions are compared with that of GA on various parameters of a Distributed Query, like up to 12 joins and 10 sites. Keywords: Distributed Query Optimization, Database Statistics, Query Execution Plan, Genetic Algorithms, Operation Allocation.
The document surveys previous work on clustering of time series data. It discusses three main approaches to time series clustering: (1) working directly with raw time series data, (2) extracting features from the raw data and clustering the features, and (3) building models from the raw data and clustering the model parameters. It also summarizes commonly used clustering algorithms, measures for determining time series similarity, and criteria for evaluating clustering results. The document categorizes and discusses past time series clustering research and identifies topics for future work. It aims to provide an overview of the field of time series clustering.
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
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Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
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Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
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Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
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With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
This document provides an overview of several clustering algorithms. It begins by defining clustering and its importance in data mining. It then categorizes clustering algorithms into four main types: partitional, hierarchical, grid-based, and density-based. For each type, some representative algorithms are described briefly. The document also reviews several popular clustering algorithms like k-means, CLARA, PAM, CLARANS, and BIRCH in more detail. It discusses aspects like the algorithms' time complexity, types of data handled, ability to detect clusters of different shapes, required input parameters, and advantages/disadvantages. Overall, the document aims to guide selection of suitable clustering algorithms for specific applications by surveying their key characteristics.
Spatial data mining involves discovering patterns from large spatial datasets. It differs from traditional data mining due to properties of spatial data like spatial autocorrelation and heterogeneity. Key spatial data mining tasks include clustering, classification, trend analysis and association rule mining. Clustering algorithms like PAM and CLARA are useful for grouping spatial data objects. Trend analysis can identify global or local trends by analyzing attributes of spatially related objects. Future areas of research include spatial data mining in object oriented databases and using parallel processing to improve computational efficiency for large spatial datasets.
A SURVEY ON OPTIMIZATION APPROACHES TO TEXT DOCUMENT CLUSTERINGijcsa
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This document provides a survey of optimization approaches that have been applied to text document clustering. It discusses several clustering algorithms and categorizes them as partitioning methods, hierarchical methods, density-based methods, grid-based methods, model-based methods, frequent pattern-based clustering, and constraint-based clustering. It then describes several soft computing techniques that have been used as optimization approaches for text document clustering, including genetic algorithms, bees algorithms, particle swarm optimization, and ant colony optimization. These optimization techniques perform a global search to improve the quality and efficiency of document clustering algorithms.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
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Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
This document provides an overview of stream data mining techniques. It discusses how traditional data mining cannot be directly applied to data streams due to their continuous, rapid nature. The document outlines some essential methodologies for analyzing data streams, including sampling, load shedding, sketching, and data summarization techniques like reservoirs, histograms, and wavelets. It also discusses challenges in applying these techniques to data streams and open problems in the emerging field of stream data mining.
Clustering is also known as data segmentation aims to partitions data set into groups, clusters, according to their similarity. Cluster analysis has been extensively studied in many researches. There are many algorithms for different types of clustering. These classical algorithms can't be applied on big data due to its distinct features. It is a challenge to apply the traditional techniques on large unstructured data. This study proposes a hybrid model to cluster big data using the famous traditional K-means clustering algorithm. The proposed model consists of three phases namely; Mapper phase, Clustering Phase and Reduce phase. The first phase uses map-reduce algorithm to split big data into small datasets. Whereas, the second phase implements the traditional clustering K-means algorithm on each of the spitted small data sets. The last phase is responsible of producing the general clusters output of the complete data set. Two functions, Mode and Fuzzy Gaussian, have been implemented and compared at the last phase to determine the most suitable one. The experimental study used four benchmark big data sets; Covtype, Covtype-2, Poker, and Poker-2. The results proved the efficiency of the proposed model in clustering big data using the traditional K-means algorithm. Also, the experiments show that the Fuzzy Gaussian function produces more accurate results than the traditional Mode function.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
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In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
Windows 8 javascript apps ā getting it rightbrendankowitz
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This document summarizes key points about developing Windows 8 JavaScript apps using WinJS. It discusses why to use WinJS, getting started, differences from web development, third party controls, testing, using C# libraries, asynchronous programming, storage options, debugging, and common gotchas. The document provides an overview of concepts and best practices for WinJS app development.
This document discusses the advantages of blastocyst stage embryo transfer compared to cleavage stage embryo transfer, particularly in the context of oocyte donation. Key points include:
- Blastocyst stage transfer has been shown to improve outcomes like implantation and pregnancy rates compared to cleavage stage transfer based on evidence from IVF studies.
- In oocyte donation specifically, studies have found significantly higher implantation and pregnancy rates per embryo transfer with blastocyst stage transfer compared to cleavage stage.
- The development of improved embryo culture systems has made successful blastocyst development and transfer more routine. Blastocyst transfer also allows more information about embryo developmental potential.
The new public health and std hiv preventionSpringer
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This document discusses social determinants of sexually transmitted infections. It explores how social factors like education, occupation, neighborhoods, and media can influence sexual behaviors and networks, thereby affecting STI spread. Key determinants of STI transmission include likelihood of transmission during sex, number of sexual partners, and partnership patterns. Factors like consistent condom use, access to healthcare, sex education, sexual network patterns, and timing of partnerships all influence STI rates at a population level.
Building windows phone 7 apps for the enterprisebrendankowitz
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This document discusses building Windows Phone 7 apps for enterprise use. It introduces the need for mobile apps in enterprises where employees need access to data and notifications while in the field. It then describes building a sample app called P.L.I.M.S for an environmental testing company called EnviroCorp that allows scientists and lab managers mobile access to test results and notifications. The document covers designing the mobile app, building backend services on Azure, consuming those services from the phone, deploying to enterprise users, and considering alternative platforms like HTML5.
This document contains a collection of short passages on various topics ranging from silence, speaking purposefully, observing others, managing time, choices over time, cooking with care, and emancipation leading to generosity. The passages are disjointed and cover unrelated subjects in an abstract manner.
Blockbuster Launch Evolution - Chris Bogan Keynote-FINAL (09-24-2015)Chris Bogan
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This document discusses the evolution of successful blockbuster launches and market entry strategies. It begins with an overview of Gilead's Hepatitis C franchise and how Sovaldi and Harvoni significantly outperformed previous blockbuster launches in revenue in their first year. It then discusses how small variations in launch can impact outcomes and the need for market research to understand opportunities. Finally, it notes that blockbuster launches have shifted from primary care to more targeted specialty care models focused on access, education, and understanding niche markets.
The frequency spectrum of a time-domain signal is a representation of that signal in the frequency domain. The frequency spectrum can be generated via a Fourier transform of the signal, and the resulting values are usually presented as amplitude and phase, both plotted versus frequency.
Structural and oct changes in diabetic retinopathy1suchismita Rout
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1) Diabetic retinopathy is caused by chronic hyperglycemia and results in progressive retinal dysfunction. Prolonged hyperglycemia damages blood vessels and the blood-retinal barrier.
2) OCT is useful for diagnosing and monitoring diabetic retinopathy. It provides high-resolution cross-sectional images of the retina to assess structural changes over time.
3) Features of diabetic retinopathy on OCT include macular edema, hard exudates, neurosensory detachment, and changes associated with proliferative retinopathy like tractional retinal detachment.
El consumidor digital cada vez compra mĆ”s en Internet y las empresas (fabricantes / marcas) estĆ”n lanzando de forma masiva plataformas de Ecommerce. En la Openclass virtual gratuita āECOMMERCE MANAGER: UNA PROFESIĆN DE FUTURO EN EL MUNDO DIGITALā aprenderĆ”s de la mano de Jorge GonzĆ”lez Marcos, fundador de www.ecommercerentable.es
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
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The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
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The document discusses techniques for rainfall prediction using data mining. It provides an overview of various data mining techniques that have been used for rainfall forecasting, including neural networks and SARIMA (Seasonal Autoregressive Integrated Moving Average) time series models. The document then describes applying both a multilayer perceptron neural network and SARIMA models to monthly rainfall data from regions in India to perform forecasting, and comparing the results of the two techniques.
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONijdms
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This document discusses fuzzy statistical databases and their physical organization. It introduces the concept of fuzzy cardinality for counting elements in a fuzzy set or fuzzy relation. A fuzzy statistical database is proposed to store fuzzy statistics generated from fuzzy relational databases. This fuzzy statistical database contains fuzzy statistical tables, which can be type-1 or type-2 depending on the complexity of the fuzzy attributes. The physical organization of these fuzzy statistical tables is discussed to facilitate efficient storage and retrieval of the imprecise statistical data.
Influence over the Dimensionality Reduction and Clustering for Air Quality Me...IJAEMSJORNAL
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The current trend in the industry is to analyze large data sets and apply data mining, machine learning techniques to identify a pattern. But the challenges with huge data sets are the high dimensions associated with it. Sometimes in data analytics applications, large amounts of data produce worse performance. Also, most of the data mining algorithms are implemented column wise and too many columns restrict the performance and make it slower. Therefore, dimensionality reduction is an important step in data analysis. Dimensionality reduction is a technique that converts high dimensional data into much lower dimension, such that maximum variance is explained within the first few dimensions. This paper focuses on multivariate statistical and artificial neural networks techniques for data reduction. Each method has a different rationale to preserve the relationship between input parameters during analysis. Principal Component Analysis which is a multivariate technique and Self Organising Map a neural network technique is presented in this paper. Also, a hierarchical clustering approach has been applied to the reduced data set. A case study of Air quality measurement has been considered to evaluate the performance of the proposed techniques.
Data repository for sensor network a data mining approachijdms
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The development of sensor data repositories will aid the researchers to create benchmark dataset. These
benchmark dataset will provide a platform for all the researchers to access the data, test and compare the
accuracy of their algorithms. However, the storage and management of sensor data itself is a challenging
task due to various reasons such as noisy, redundant, missing, and faulty data. Therefore it is very
important to create a data repository which contains the precise and accurate data and also storage and
management of data is effective. Hence, in this paper we are proposing to use the combination of
quantitative association rules and decision tree for classification of faulty data and normal data. Usage of
multiple linear regression models for the estimation of missing data. A symbolic table approach for storage
and management of sensor data. And development of a graphical user interface for visualization of sensor
data.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
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Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Efficiency of recurrent neural networks for seasonal trended time series mode...IJECEIAES
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Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a
non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
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selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common informationās and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
A framework for mining signatures from event sequences and its applications i...JPINFOTECH JAYAPRAKASH
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This paper proposes a novel temporal event matrix representation (TEMR) framework to perform temporal signature mining from longitudinal event data. TEMR represents event data as a spatial-temporal matrix, where one dimension is event type and the other is time. A doubly sparse convolutional matrix approximation is used to detect latent signatures in the data. The approach is validated on synthetic and electronic health record data, showing it can scale to large datasets and detect interpretable signatures.
CLUSTER DETECTION SCHEMES IN SPATIO TEMPORAL NETWORKSIJDKP
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A spatiotemporal challenge can be portrayed as an inquiry that has no short of what one spatial and one
momentary property. The spatial properties are region and geometry of the inquiry. The transient property
is timestamp or time interval for which the challenge is real. The spatio fleeting inquiry as a general rule
contains spatial, common and topical or non-spatial properties. Instances of such inquiries are moving
auto, forest fire, and earth shake. Spatiotemporal educational accumulations essentially find changing
estimations of spatial and topical attributes over a time allotment. Spatio transient bunching is a procedure
of collection articles in view of their spatial and worldly similitude. It is generally new sub-field of
information mining which increased high notoriety particularly in geographic data sciences because of the
inescapability of a wide range of area based or ecological gadgets that record position, time or/and
natural properties of a protest or set of articles progressively. As a result, distinctive sorts and a lot of
spatio-transient information got to be distinctly accessible that acquaint new difficulties with information
examination and require novel ways to deal with learning revelation.
A Time Series ANN Approach for Weather Forecastingijctcm
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Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
APPLICATION OF MATRIX PROFILE TECHNIQUES TO DETECT INSIGHTFUL DISCORDS IN CLI...ijscai
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This document summarizes a research paper that proposes a new approach to detect prominent discords, or anomalous patterns, in large climate data time series. The approach introduces the concept of a prominent discord, which is the most significant discord found across different window sizes that all start at the same position. It presents methods to compute prominent discords exactly using STOMP or approximately using SCRIMP++. The approach is applied to over 100 years of monthly climate impact runoff data to detect insightful discords. It compares the exact and approximate methods and explores the tradeoff between accuracy and computational efficiency.
The definition and extraction of actionable anomalous discords, i.e. pattern outliers, is a challenging
problem in data analysis. It raises the crucial issue of identifying criteria that would render a discord
more insightful than another one. In this paper, we propose an approach to address this by
introducing the concept of prominent discord. The core idea behind this new concept is to identify
dependencies among discords of varying lengths. How can we identify a discord that would be
prominent? We propose an ordering relation, that ranks discords, and we seek a set of prominent
discords with respect to this ordering. Our contributions are threefold 1) a formal definition,
ordering relation and methods to derive prominent discords based on Matrix Profile techniques,2)
their evaluation over large contextual climate data, covering 110 years of monthly data, and 3) a
comparison of an exact method based on STOMP and an approximate approach that is based on
SCRIMP++ to compute the prominent discords and study the tradeoff optimality/CPU. The
approach is generic and its pertinence shown over historical climate data.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges in doing so. It then proposes combining k-means clustering with Voronoi diagrams to improve the performance of k-means when clustering uncertain data. Specifically, it suggests using k-means to generate clusters and Voronoi diagrams to answer nearest neighbor queries, in order to minimize computation time. Finally, it concludes that integrating clustering algorithms with indexing methods can effectively cluster uncertain data objects.
This document discusses clustering of uncertain data objects. It first provides background on clustering uncertain data and challenges involved. It then reviews various existing approaches for clustering uncertain data, including using soft classifiers and probabilistic databases. The document proposes combining k-means clustering with Voronoi diagrams and indexing techniques to improve the performance and efficiency of clustering uncertain datasets. It outlines a plan to integrate k-means with Voronoi diagrams and indexing to reduce execution time and increase clustering performance and results for uncertain data. Finally, it concludes that combining clustering with indexing approaches can better handle uncertain data clustering challenges.
A frame work for clustering time evolving dataiaemedu
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The document proposes a framework for clustering time-evolving categorical data using a sliding window technique. It uses an existing clustering algorithm (Node Importance Representative) and a Drifting Concept Detection algorithm to detect changes in cluster distributions between the current and previous data windows. If a threshold difference in clusters is exceeded, reclustering is performed on the new window. Otherwise, the new clusters are added to the previous results. The framework aims to improve on prior work by handling drifting concepts in categorical time-series data.
10 9242 it geo-spatial information for managing ambiguity(edit ty)IAESIJEECS
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An innate test emerging in any dataset containing data of space as well as time is vulnerability due to different wellsprings of imprecision. Incorporating the effect of the instability is a principal while evaluating the unwavering quality (certainty) of any question result from the hidden information. To bargain with vulnerability, arrangements have been proposed freely in the geo-science and the information science look into group. This interdisciplinary instructional exercise crosses over any barrier between the two groups by giving an exhaustive diagram of the distinctive difficulties required in managing indeterminate geo-spatial information, by looking over arrangements from both research groups, and by distinguishing likenesses, cooperative energies and open research issues.
A Critique Of Anscombe S Work On Statistical Analysis Using Graphs (2013 Home...Simar Neasy
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This document provides a critique of Anscombe's 1973 work on using graphs for statistical analysis. It discusses how graphs can provide insight into data beyond calculations by revealing hidden properties. While researchers often focus only on computations, graphs from exploratory data analysis can generate hypotheses. Anscombe showed four datasets with identical statistics but different graphics, highlighting how visualizing data is important. The document advocates using graphical modeling techniques like Bayesian and Markov networks to decompose variable relationships and support inferences. It emphasizes that exploratory graphical analysis remains important alongside more sophisticated techniques.
Survey Paper on Clustering Data Streams Based on Shared Density between Micro...IRJET Journal
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This document discusses a survey of clustering data streams based on shared density between micro-clusters. It describes how current reclustering approaches for data stream clustering ignore density information between micro-clusters, which can result in inaccurate cluster assignments. The paper proposes DBSTREAM, a new approach that captures shared density between micro-clusters using a density graph. This density information is then used in the reclustering process to generate final clusters based on actual density between adjacent micro-clusters rather than assumptions about data distribution.
Similar to Learning in non stationary environments (20)
The chemistry of the actinide and transactinide elements (set vol.1 6)Springer
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Actinium is the first member of the actinide series of elements according to its electronic configuration. Actinium closely resembles lanthanum chemically. The three most important isotopes of actinium are 227Ac, 228Ac, and 225Ac. 227Ac is a naturally occurring isotope in the uranium-actinium decay series with a half-life of 21.772 years. 228Ac is in the thorium decay series with a half-life of 6.15 hours. 225Ac is produced from 233U with applications in medicine.
Transition metal catalyzed enantioselective allylic substitution in organic s...Springer
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This document provides an overview of computational studies of palladium-mediated allylic substitution reactions. It discusses the history and development of quantum mechanical and molecular mechanical methods used to study the structures and reactivity of allyl palladium complexes. In particular, density functional theory methods like B3LYP have been widely used to study reaction mechanisms and factors controlling selectivity. Continuum solvation models have also been important for properly accounting for reactions in solvent.
1) Ranchers in Idaho observed lambs born with cyclopia (one eye) due to ewes grazing on corn lily plants. Cyclopamine was identified as the compound responsible and was later found to inhibit the Hedgehog signaling pathway.
2) Nakiterpiosin and nakiterpiosinone were isolated from cyanobacterial sponges and shown to inhibit cancer cell growth. Their unique C-nor-D-homosteroid skeleton presented synthetic challenges.
3) The authors developed a convergent synthesis of nakiterpiosin involving a carbonylative Stille coupling and a photo-Nazarov cyclization. Model studies led them to propose a revised structure for n
This document reviews solid-state NMR techniques that have been used to determine the molecular structures of amyloid fibrils. It discusses five categories of NMR techniques: 1) homonuclear dipolar recoupling and polarization transfer via J-coupling, 2) heteronuclear dipolar recoupling, 3) correlation spectroscopy, 4) recoupling of chemical shift anisotropy, and 5) tensor correlation methods. Specific techniques described include rotational resonance, dipolar dephasing, constant-time dipolar dephasing, REDOR, and fpRFDR-CT. These techniques have provided insights into the hydrogen-bond registry, spatial organization, and backbone torsion angles of amyloid fibrils.
This document discusses principles of ionization and ion dissociation in mass spectrometry. It covers topics like ionization energy, processes that occur during electron ionization like formation of molecular ions and fragment ions, and ionization by energetic electrons. It also discusses concepts like vertical transitions, where electronic transitions occur much faster than nuclear motions. The document provides background information on fundamental gas phase ion chemistry concepts in mass spectrometry.
Higher oxidation state organopalladium and platinumSpringer
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This document discusses the role of higher oxidation state platinum species in platinum-mediated C-H bond activation and functionalization. It summarizes that the original Shilov system, which converts alkanes to alcohols and chloroalkanes under mild conditions, involves oxidation of an alkyl-platinum(II) intermediate to an alkyl-platinum(IV) species by platinum(IV). This "umpolung" of the C-Pt bond facilitates nucleophilic attack and product formation rather than simple protonolysis back to alkane. Subsequent work has validated this mechanism and also demonstrated that platinum(IV) can be replaced by other oxidants, as long as they rapidly oxidize the
Principles and applications of esr spectroscopySpringer
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- Electron spin resonance (ESR) spectroscopy is used to study paramagnetic substances, particularly transition metal complexes and free radicals, by applying a magnetic field and measuring absorption of microwave radiation.
- ESR spectra provide information about electronic structure such as g-factors and hyperfine couplings by measuring resonance fields. Pulse techniques also allow measurement of dynamic properties like relaxation.
- Paramagnetic species have unpaired electrons that create a magnetic moment. ESR detects transition between spin energy levels induced by microwave absorption under an applied magnetic field.
This document discusses crystal structures of inorganic oxoacid salts from the perspective of periodic graph theory and cation arrays. It analyzes 569 crystal structures of simple salts with the formulas My(LO3)z and My(XO4)z, where M are metal cations, L are nonmetal triangular anions, and X are nonmetal tetrahedral anions. The document finds that in about three-fourths of the structures, the cation arrays are topologically equivalent to binary compounds like NaCl, NiAs, and FeB. It proposes representing these oxoacid salts as a quasi-binary model My[L/X]z, where the cation arrays determine the crystal structure topology while the oxygens play a
Field flow fractionation in biopolymer analysisSpringer
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This document summarizes a study that uses flow field-flow fractionation (FlFFF) to measure initial protein fouling on ultrafiltration membranes. FlFFF is used to determine the amount of sample recovered from membranes and insights into how retention times relate to the distance of the sample layer from the membrane wall. It was observed that compositionally similar membranes from different companies exhibited different sample recoveries. Increasing amounts of bovine serum albumin were adsorbed when the average distance of the sample layer was less than 11 mm. This information can help establish guidelines for flow rates to minimize fouling during ultrafiltration processes.
1) The document discusses phonons, which are quantized lattice vibrations in crystals that carry thermal energy. It describes modeling crystal vibrations using a harmonic lattice approach.
2) Normal modes of the lattice vibrations can be described as a set of independent harmonic oscillators. Quantum mechanically, these normal modes are quantized as phonons with discrete energy levels.
3) Phonons can be thought of as quasiparticles that carry momentum and energy in the crystal lattice. Their propagation is described using a phonon field approach rather than independent normal modes.
This chapter discusses 3D electroelastic problems and applied electroelastic problems. For 3D problems, it presents the potential function method for solving problems involving a penny-shaped crack and elliptic inclusions. It derives the governing equations and introduces potential functions to obtain the general static and dynamic solutions. For applied problems, it discusses simple electroelastic problems, laminated piezoelectric plates using classical and higher-order theories, and piezoelectric composite shells. It also presents a unified first-order approximate theory for electro-magneto-elastic thin plates.
Tensor algebra and tensor analysis for engineersSpringer
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This document discusses vector and tensor analysis in Euclidean space. It defines vector- and tensor-valued functions and their derivatives. It also discusses coordinate systems, tangent vectors, and coordinate transformations. The key points are:
1. Vector- and tensor-valued functions can be differentiated using limits, with the derivatives being the vector or tensor equivalent of the rate of change.
2. Coordinate systems map vectors to real numbers and define tangent vectors along coordinate lines.
3. Under a change of coordinates, components of vectors and tensors transform according to the Jacobian of the coordinate transformation to maintain geometric meaning.
This document provides a summary of carbon nanofibers:
1) Carbon nanofibers are sp2-based linear filaments with diameters of around 100 nm that differ from continuous carbon fibers which have diameters of several micrometers.
2) Carbon nanofibers can be produced via catalytic chemical vapor deposition or via electrospinning and thermal treatment of organic polymers.
3) Carbon nanofibers exhibit properties like high specific area, flexibility, and strength due to their nanoscale diameters, making them suitable for applications like energy storage electrodes, composite fillers, and bone scaffolds.
Shock wave compression of condensed matterSpringer
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This document provides an introduction and overview of shock wave physics in condensed matter. It discusses the assumptions made in treating one-dimensional plane shock waves in fluids and solids. It briefly outlines the history of the field in the United States, noting that accurate measurements of phase transitions from shock experiments established shock physics as a discipline and allowed development of a pressure calibration scale for static high pressure work. It describes some of the practical applications of shock wave experiments for providing high-pressure thermodynamic data, understanding explosive detonations, calibrating pressure scales, and enabling studies of materials under extreme conditions.
Polarization bremsstrahlung on atoms, plasmas, nanostructures and solidsSpringer
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This document discusses the quantum electrodynamics approach to describing bremsstrahlung, or braking radiation, of a fast charged particle colliding with an atom. It derives expressions for the amplitude of bremsstrahlung on a one-electron atom within the first Born approximation. The amplitude has static and polarization terms. The static term corresponds to radiation from the incident particle in the nuclear field, reproducing previous results. The polarization term accounts for radiation from the atomic electron and contains resonant denominators corresponding to intermediate atomic states. The full treatment allows various limits to be taken, such as removing the nucleus or atomic electron, reproducing known results from quantum electrodynamics.
Nanostructured materials for magnetoelectronicsSpringer
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This document discusses experimental approaches to studying magnetization and spin dynamics in magnetic systems with high spatial and temporal resolution.
It describes using time-resolved X-ray photoemission electron microscopy (TR-XPEEM) to image the temporal evolution of magnetization in magnetic thin films with picosecond time resolution. Results are presented showing the changing domain structure in a Permalloy thin film following excitation with a magnetic field pulse. Different rotation mechanisms are observed depending on the initial orientation of the magnetization with respect to the applied field.
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This document discusses nanomaterials for biosensors and implantable biodevices. It describes how nanostructured thin films have enabled the development of more sensitive electrochemical biosensors by improving the detection of specific molecules. Two common techniques for creating nanostructured thin films are described - Langmuir-Blodgett films and layer-by-layer films. These techniques allow for the precise control of film thickness at the nanoscale and have been used to immobilize biomolecules like enzymes to create biosensors. Recent research is also exploring how these nanostructured films and biomolecules can be used to create implantable biosensors for real-time monitoring inside the body.
Modern theory of magnetism in metals and alloysSpringer
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This document provides an introduction to magnetism in solids. It discusses how magnetic moments originate from electron spin and orbital angular momentum at the atomic level. In solids, electron localization determines whether magnetic properties are described by localized atomic moments or collective behavior of delocalized electrons. The key concepts of metals and insulators are introduced. The document then presents the basic Hamiltonian used to describe magnetism in solids, including terms for kinetic energy, electron-electron interactions, spin-orbit coupling, and the Zeeman effect. It also discusses how atomic orbitals can be used as a basis set to represent the Hamiltonian and describes the symmetry properties of s, p, and d orbitals in cubic crystals.
This chapter introduces and classifies various types of damage that can occur in structures. Damage can be caused by forces, deformations, aggressive environments, or temperatures. It can occur suddenly or over time. The chapter discusses different damage mechanisms including corrosion, excessive deformation, plastic instability, wear, and fracture. It also introduces concepts that will be covered in more detail later such as damage mechanics, fracture mechanics, and the influence of microstructure on damage and fracture. The chapter aims to provide an overview of damage types before exploring specific mechanisms and analyses in later chapters.
This document summarizes research on identifying spin-wave eigen-modes in a circular spin-valve nano-pillar using Magnetic Resonance Force Microscopy (MRFM). Key findings include:
1) Distinct spin-wave spectra are observed depending on whether the nano-pillar is excited by a uniform in-plane radio-frequency magnetic field or by a radio-frequency current perpendicular to the layers, indicating different excitation mechanisms.
2) Micromagnetic simulations show the azimuthal index Ļ is the discriminating parameter, with only Ļ=0 modes excited by the uniform field and only Ļ=+1 modes excited by the orthogonal current-induced Oersted field.
3) Three indices are used to label resonance
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
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Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
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DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of whatās possible in finance.
In summary, DeFi in 2024 is not just a trend; itās a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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Are you ready to revolutionize how you handle data? Join us for a webinar where weāll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, weāll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sourcesāfrom PDF floorplans to web pagesāusing FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether itās populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
Weāll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power gridās behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
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* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
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Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
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GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Donāt worry, we can help with all of this!
Weāll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. Weāll provide examples and solutions for those as well. And naturally weāll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Ā
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind fĆ¼r viele in der HCL-Community seit letztem Jahr ein heiĆes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und LizenzgebĆ¼hren zu kƤmpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer mƶglich. Das verstehen wir und wir mƶchten Ihnen dabei helfen!
Wir erklƤren Ihnen, wie Sie hƤufige Konfigurationsprobleme lƶsen kƶnnen, die dazu fĆ¼hren kƶnnen, dass mehr Benutzer gezƤhlt werden als nƶtig, und wie Sie Ć¼berflĆ¼ssige oder ungenutzte Konten identifizieren und entfernen kƶnnen, um Geld zu sparen. Es gibt auch einige AnsƤtze, die zu unnƶtigen Ausgaben fĆ¼hren kƶnnen, z. B. wenn ein Personendokument anstelle eines Mail-Ins fĆ¼r geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche FƤlle und deren Lƶsungen. Und natĆ¼rlich erklƤren wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt nƤherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Ćberblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und Ć¼berflĆ¼ssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps fĆ¼r hƤufige Problembereiche, wie z. B. Team-PostfƤcher, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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An English š¬š§ translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech šØšæ version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
Ā
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
2. 58 W. Pedrycz et al.
3.1 Introduction
Spatiotemporal data become visible in numerous applications. We envision data
distributed in time and space: readings associated with oil wells distributed over
space and reported in time, recordings of sensors distributed over some battleļ¬eld
area or disaster region, counts of animal disease recorded in several counties over
time, etc.āall of those are compelling examples of spatiotemporal data.
Spatial and temporal phenomena (systems) can be effectively analyzed and
described at various levels of abstraction. The selected level of abstraction is of
paramount importance as it helps realize a user-friendly approach to data analysis. In
essence, the user can establish the most suitable perspective to view the data (iden-
tifying a suitable level of detail to be included in the analysis); making an overall
analysis more humancentric. This view inherently brings a concept of information
granules into the picture. Information granules, no matter how they are formalized
(either as sets, fuzzy sets, rough sets, or others) are regarded as a collection of
conceptual landmarks using which one looks at the data and describes them. We
show that the relational way of capturing data naturally invokes the mechanisms of
relation calculus where the concepts of granulation and degranulation are crucial to
the optimization of the vocabulary (codebook) of information granules by means
of which the main dependencies in data become revealed and quantiļ¬ed. The key
objective of this study is to introduce a relational way of data analysis through their
granulation and degranulation and show a constructive way of forming information
granules leading to the best granular data representation. Furthermore, we introduce
some way of characterizing relationships between granular codebooks pertaining to
the description of data collected in successive time frames.
The presentation of the material is organized in a topādown fashion. We start
with an illustrative practical example (Sect. 3.2) and then move on to the problem
statement (Sect. 3.3) by stressing the relational character of data to be processed.
A mechanism of granulation of information and a way of representing data in the
language of information granules is covered in Sect. 3.4, which leads to a concept
of a so-called granular signature of data (Sect. 3.5). The fundamental concept of
information granulationādegranulation is introduced in Sect. 3.6 where we also
show how a vocabulary (codebook) of information granules can be optimized
so that the reconstruction of data implies a minimal value of the reconstruction
error. With regard to this optimization, it is discussed how fuzzy clustering leads
to the realization of information granules (Sect. 3.7). The issue of evolvability of
the relational description of data emerging in presence of a series of data frames
reported in successive time slices is covered in Sect. 3.8.
3. 3 A Granular Description of Data: A Study in Evolvable Systems 59
3.2 From Conceptual Developments of Information Granules
to Applications
As an example of a real-world problem whose formulation and solutions can be
naturally cast within the conceptual framework to be discussed here, relates to
a comprehensive analysis of spatiotemporal data and associated event detection
schemes formed directly on a basis of information granules. An interesting system
where we encounter a wealth of spatiotemporal data is found at http://www1.agric.
gov.ab.ca/department/$deptdocs.nsf/all/cl12944. It concerns a set of data collection
stations distributed across the province of Alberta, see Fig. 3.1. This system helps
graph, compare, and download data in almost real-time mode for more than
270 stations where data themselves go back to April 2005. There are some
other data sets like precipitation and temperature (normal), temperature extremes,
solar radiation, soil temperature, and other important weather characteristics. In
a nutshell, we encounter a collection of spatial dataāstations identiļ¬ed by some
xāy coordinates. For each station, there is an associated suite of time series (say,
dealing with the temperature, precipitation, radiation), see Fig. 3.2. An analysis
Fig. 3.1 A screen snapshot of the AgroClimatic Information Service (ACIS)
4. 60 W. Pedrycz et al.
Fig. 3.2 Collection stations along with associated time series
Fig. 3.3 From collection
stations to spatiotemporal
information granules
(clusters)
of such spatiotemporal data offers interesting and practically relevant insights into
their nature and helps identify main trends and relationships. Several challenging
problems are worth identifying in this context:
(a) Detection of information granules in the spatiotemporal data space, Fig. 3.3.
As shown there, the resulting information granules form regions (information
granules) in the space xāy by bringing together points (stations) characterized
by similar temporal data (time series) and being in close vicinity to each
other. From the algorithmic perspective, one can envision using a clustering
algorithm (which has to be carefully designed to capture the spatial as well as
5. 3 A Granular Description of Data: A Study in Evolvable Systems 61
Fig. 3.4 Evolution of information granules through revealing dependencies among information
granules observed in successive discrete time moments (determined by the predetermined length
of the time horizon T)
temporal component). This implies that we have to carefully specify a distance
function used to quantify the closeness of elements in the spatiotemporal data
space. From the perspective of practical application, by forming the information
granules the user can get a better insight into the regions of the province, their
location, and size which exhibit some level of inner homogeneity while being
distinct from the others. Such ļ¬ndings could be of immediate value to decision-
makers in agriculture sector.
(b) Alluding to the constructs noted above, it is apparent that the temporal compo-
nent of the data depends directly on the length of the corresponding time series.
In particular, this impacts a way in which time series are represented (as the
time series could be either stationary or nonstationary, which is usually the case
when dealing with longer time horizons for which the information granules are
to be formed). This brings a concept of the temporal scale (which again triggers
some temporal granularity). The information granules are constructed over
some time horizon. They evolve so in successive time moments, say T, 2T, . . .
we obtain a collection of information granules, see Fig. 3.4.
These information granules are related. By describing relationships (dependen-
cies) between the information granules arising in time horizons T, 2T, etc., an
interesting dynamic pattern of changes (dynamics) at a more abstract level can be
revealed. It could be regarded as a generalization of the dynamic process where now
the dynamics is captured at a certain nonnumeric level and with the varying number
of granules, we witness changes in the level of speciļ¬city of the granular descriptors
(which becomes reļ¬ective of the varying level of complexity of the associated time
series).
The data collected in the system described above can be used in conjunction with
other sources of data (e.g., related with livestock and humans in the realization of
event detection systems). Those are essential to prevention of disasters and effective
risk management for livestock production, public health, and rural communities.
The need for animal health surveillance systems and ensuing decision-making
architectures capable of rapid disease detection is of paramount relevance. This is
6. 62 W. Pedrycz et al.
exacerbated by a number of factors. The speed of transportation has never been
faster, facilitating the rapid spread of pathogens around the world, and makes timely
detection more difļ¬cult. The average time to transport people and goods around the
world is less than the incubation period for most infectious diseases. The volume
of people, animals, and animal products that is moved everyday is large. Event
detectors, which could be sought as an integral functional component are formalized
and described in the language of information granules and are built on a basis of
reported weather data, disease cases reported and a way of their spread as well as
an intensity of spread itself [3ā5, 7, 8]. All these phenomena can be conveniently
described and operated on through the calculus of information granules.
3.3 Problem Formulation
The data we are concerned with are of spatiotemporal character. The temporal data
(time series) are associated with some geographical xāy coordinates. Their descrip-
tion is provided in the form of a certain vector z positioned in the p-dimensional
unit hypercube. We can encounter here a genuine diversity of available ways used
to describe time series such as parameters of the autoregressive time series model
(AR model), coefļ¬cients of the Fourier expansion, cepstral coefļ¬cients, components
of discrete wavelet transforms (DWT), parameters of fuzzy rule-based system (in
case of fuzzy modeling of the series), and others. For instance, in case of the AR
model we have z = [a0 a1 a2 . . . as ]T where the model comes in the form z(K + 1) =
āsj=0 a j z(K ā j). One could also encounter models that are more specialized to ļ¬t a
certain category of signals such as this is the case in ECG signals modeled in terms
of Hermite expansion. In all these cases, we assume that the values of the descriptors
have been normalized so that the results are located in the [0, 1] p hypercube.
The available data come in the form of the triples (xk , yk , zk ) with xk = [xk yk ]T
being the spatial coordinates of the temporal data zk , k = 1, 2, . . . , N. The equivalent
representation that is convenient in our considerations, comes in a form of a fuzzy
relation R(x, y, z) assuming vector values in [0, 1] p for speciļ¬c values of xk and yk .
In other words, the above notation underlines that the pair (xk , yk ) is associated with
a vector of temporal data zk . An illustration of an example fuzzy relation is included
in Fig. 3.5.
Given the relational data R, we are interested in the following two important
characterization and design issues:
(a) A description of R realized in a terms of a certain vocabulary of granular
descriptors, see Fig. 3.6. We form a certain focused view at the data provided
by the vocabulary. It has to be noted, as shown in Fig. 3.6, that different
vocabularies (codebooks) facilitate different, sometimes complementary views
at the available experimental evidence.
(b) Analysis of granulation and degranulation capabilities of the vocabularies. The
intriguing question is about capabilities of the given vocabulary to capture the
essence of the data (granular descriptors). Subsequently based upon the granular
7. 3 A Granular Description of Data: A Study in Evolvable Systems 63
Fig. 3.5 Visualization of the fuzzy relation R; associated with some combination of the (x, y)-
coordinates are sineālike time series characterized, e.g., by a collection of their Fourier expansion
coefļ¬cients
Fig. 3.6 Formation of various views at relational data supported by the use of different
vocabularies
representation of the data, we discuss how to reconstruct (degranulate) the
original data, see Fig. 3.7. The quality of this reconstruction (as we anticipate
that the process of reconstruction could impart some losses) can be quantiļ¬ed
and the ļ¬gure of merit associated with this process can be used in the
optimization of the codebook.
3.4 Granulation of Information and Granular Data
Representation
We characterize the available data R in terms of a collection of information granules-
semantically meaningful entities [1, 2, 6, 17] that are formed in the spatial and
temporal coordinates. Formally, information granules can be captured in different
ways, say in the form of sets, rough sets, fuzzy sets, to name a few alternatives.
Here we are concerned with their realization by means of fuzzy sets. We deļ¬ne a
8. 64 W. Pedrycz et al.
Fig. 3.7 The concept of
granulation and degranulation
realized with the aid of a
vocabulary of information
granules
collection of spatial information granules (located in the x- and y-coordinates) and
temporal information granules used to describe the spatiotemporal signal
Ai : R ā [0, 1], i = 1, 2, . . . , n
B j : R ā [0, 1], j = 1, 2, . . . , m
Cl : [0, 1] p ā [0, 1] p, l = 1, 2, . . . , r
Those are fuzzy sets described by the corresponding membership functions, that
is Ai (x), B j (y), and Cl (z). The collections of these granules are organized in the
form of families of fuzzy sets A, B,C, respectively. For instance, we have A =
{A1 , A2 , . . . , An }, B = {B1 , B2 , . . . , Bm } and C = {C1 ,C2 , . . . ,Cr }. These fuzzy sets
come with a well-deļ¬ned meaning. For instance, we may have linguistic descriptors
such as
ā¢ Ai āeast region of x-coordinate, B j ānorth region of the y-coordinate.
ā¢ Cl āhigh frequency and low amplitude of the ļ¬rst component of the Fourier
expansion of the signal.
See Fig. 3.8. We form Cartesian products of the granular descriptors. For instance,
the Cartesian product of Ai and B j deļ¬ned above, the Cartesian product Ai Ć B j ,
can be regarded as a descriptor of the region of x ā y space located in the north-east
region of the plane.
The Cartesian product of the above information granules can be treated as
a granular descriptorāa linguistic āprobeā used to describe the available data.
A collection of all probes is used to describe/perceive the essence of data. Taken
altogether they can be sought as a granular codebook (vocabulary) supporting a
concise description of the collected experimental evidence.
The number of information granules for the spatial aspect of data could be
reduced if we consider treating both coordinates en block meaning that a family of
fuzzy sets is with respect to x- and y-coordinate. This gives rise to the membership
functions of both arguments, say A1 (x, y), A2 (x, y), . . . .
9. 3 A Granular Description of Data: A Study in Evolvable Systems 65
Fig. 3.8 Examples of linguistic descriptors used in the granular representation of relational data:
spatial granular descriptors Ai and B j (a) and spatial granular descriptor Cl (b)
3.5 A Granular Signature (Descriptor) of Relational Data
Given the collection of information granulesāvocabularies {Ai , B j ,Cl }i =
1, 2, . . . , n; j = 1, 2, . . . , m; l = 1, 2, .., r they are used to describe (represent) the
relational data R. This description is realized by performing a convolution of the
elements of the vocabularies A, B, and C with the fuzzy relation of data, cf. [14]. This
convolution is realized in a way it is quite commonly present in signal processing
as well as the technology of fuzzy sets [16] that is
gi jl = (Ai Ć B j Ć Cl ) ā¦ R (3.1)
i = 1, 2 . . . , n; j = 1, 2, . . . , m; l = 1, 2, . . . , r. Expressing the fuzzy sets of the code-
books in terms of the corresponding membership functions we obtain the values of
gi jl as a result of the max-t composition operator [14] of the fuzzy sets and the fuzzy
relation
gi jl = max [Ai (xk )tB j (yk )tCl (z)tR(xk , yk , zk )] (3.2)
xk ,yk ,zk
where t stands for a certain t-norm [12] (the minimum operator can be viewed as a
particular case).
This max-t composition comes with a useful interpretation: gi jl is then a
possibility measure of the relational data with respect to the selected information
granules indexed (i, j, l), Poss(Ai , B j ,Cl ; R). The possibility measures arise here in
an explicit manner: they describe a degree of activation of a certain element of the
Cartesian product of the codebooks by the fuzzy relation R. As an example, let us
consider the fuzzy relation R with the following entries
ā” ā¤
1.0 0.7 0.2 0.3
ā£ 0.5 0.9 1.0 0.0 ā¦
0.0 0.4 0.8 1.0
10. 66 W. Pedrycz et al.
and
ā” ā¤
0.0 0.2 0.7 1.0
ā£ 1.0 0.3 0.6 0.3 ā¦
0.3 0.5 0.5 0.2
The columns of R are indexed by x-coordinates while the rows are linked with the
y-coordinate. The ļ¬rst relation includes the ļ¬rst coordinates of zk , zk1 . The second
coordinates of zk form the second matrix. With each entry of the matrix associated
are the successive descriptors of the time series; the length of the descriptor vectors
of the time series is equal to 2. For instance, considering the location at (3, 3)āentry
of the relation R, the descriptor of the time series positioned there is zk = [0.8 0.5]T
while zk = [0.0 0.3]T for the entry (4, 2).
The vocabularies formed by information granules are as follows:
ā¢ For A with n = 2, A1 = [1.0 0.5 0.2 0.0], A2 = [0.3 1.0 0.7 0.6]
ā¢ For B with m = 2, B1 = [1.0 0.6 0.3], B2 = [0.0 0.5 1.0]
ā¢ For C with r = 2, C1 = [1.0 0.2], C2 = [0.1 1.0]
Let us now compute g111 . Following the formula (3.2), we proceed with the
successive calculations, g111 = maxx,y [min(min(A1 (x), B1 (y)), maxz (min(C1 (z),
R(x, y, z)))]. The inner expressions result in the following:
ā” ā¤
1.0 0.5 0.2 0.0
(A1 Ć B1)(x, y) = 1.0 0.5 0.2 0.0 1.0 0.6 0.3 = ā£ 0.6 0.5 0.2 0.0 ā¦
T
0.0 0.0 0.0 0.0
āā” ā¤ā” ā¤ā
1.0 0.7 0.2 0.3 0.0 0.2 0.2 0.2
max(min(C1 (z), R(x, y, z))) = max āā£ 0.5 0.9 1.0 0.0 ā¦, ā£ 0.2 0.2 0.2 0.2 ā¦ā
z
0.0 0.4 0.8 1.0 0.2 0.2 0.2 0.2
ā” ā¤
1.0 0.7 0.2 0.3
= ā£ 0.5 0.9 1.0 0.2 ā¦
0.2 0.4 0.8 0.0
ā” ā¤
1.0 0.5 0.2 0.0
Finally, we ļ¬nd the minimum of the corresponding entries of ā£ 0.6 0.5 0.2 0.0 ā¦
0.0 0.0 0.0 0.0
ā” ā¤ ā” ā¤
1.0 0.7 0.2 0.3 1.0 0.5 0.2 0.0
and ā£ 0.5 0.9 1.0 0.2 ā¦, which yields the relation ā£ 0.5 0.5 0.2 0.0 ā¦. The maxi-
0.2 0.4 0.8 0.0 0.0 0.0 0.0 0.0
mum taken over all entries of this relation returns g111 = 1.0.
The result conveyed by (3.2) can be interpreted as a granular characterization
of R. Obviously, when we consider some other granular codebook, we arrive at
a different description or view of the same data. The ļ¬exibility available here is
essential; we customize the analysis of the data and the mechanism of customization
11. 3 A Granular Description of Data: A Study in Evolvable Systems 67
Fig. 3.9 Realization of the
granular signature G of
relational data R
is offered in the form of the vocabulary. The elements of gi jl being arranged into a
single fuzzy relation G = [gi jl ] can be treated as a granular signature of R realized
for a certain collection of generic information granules (codebook) A, B, C, refer
to Fig. 3.9. It is worth noting that G supplies an important compression effect: the
original data are reduced to granular data of dimensionality n ā m ā r. Obviously, the
reduction depends on the size of the codebook. The Cartesian products of elements
of A Ć B Ć C of information granules can be ordered linearly depending upon the
values of G = [gi jl ]; higher values of its entries indicate that the corresponding
entries are more consistent, supported by experimental evidence conveyed by R.
In this way, we can identify the most essential relational descriptors of R. More
speciļ¬cally, the relational data R translate into a family of triples of descriptors
coming from the Cartesian product of the vocabularies A Ć B Ć C indexed by the
associated values of possibilities:
R = {(Ai1 , B j1 ,Cl1 ), gi1 j1l1 } (3.3)
We organize the entries of the granular representations gi1 j1l1 in a nonincreasing
order; the ļ¬rst entries of the sequence provides the essential granular descriptors of
R, that is gI ā¤ gJ ā¤ gK where I = (i1 , j1 , l1 ), J = (i2 , j2 , l2 ), etc.
3.6 The Concept of GranulationāDegranulation: A Way
of Describing Quality of Vocabulary
Given the granular signature of the relational data captured by G as well as the
elements of the vocabulary itself, we can reconstruct R. The result is an immediate
consequence that (3.2) is a fuzzy relational equation and for each combination of the
elements of A and B, we arrive at an individual fuzzy relational equation indexed
by the elements of the codebook of A and B [11, 14]. The degranulation problem
associates with a solution to the system of relational equations (3.1) in which the
fuzzy sets Ai , B j , Cl as well as the entries of the fuzzy relation gi jl are given while
the fuzzy relation R is to be determined. The solution to the problem comes as the
maximal fuzzy relation expressed as follows [10]:
R = |i, j,l ((Ai Ć B j Ć Cl )Ī¦ gi jl )
Ė (3.4)
12. 68 W. Pedrycz et al.
One can show that there is a solution to the system of fuzzy relational equations
expressed by (3.1). In terms of the membership values of the fuzzy relation, we
obtain
R(x, y, z) = mini, j,1 [(Ai (x)tB j (y)tC1 (z))Ī¦ gi jl ]
Ė (3.5)
The pseudo-residual operator [10] associated with a given t-norm is deļ¬ned in the
following form:
aĪ¦ b = sup{c ā [0, 1]|atc ā¤ b} (3.6)
The expression (3.8) requires some attention as we encounter here the vector-
valued membership function Cl(z). The calculations realized there are considered
Ė
coordinate-wise that is we compute consecutive entries of R for the arguments
z1 , z2 , . . . , z p by computing the corresponding expressions. For z1
R(x, y, z) = mini, j [(Ai (x)tB j (y)tC1 (z1 ))Ī¦ gi j1 ]
Ė (3.7)
for z2
R(x, y, z) = mini, j [(Ai (x)tB j (y)tC1 (z2 ))Ī¦ gi j2 ]
Ė (3.8)
In case we use the minimum operator, the corresponding pseudo-inverse (3.6) reads
as follows:
b a>b
aĪ¦ b = sup{c ā [0, 1]|min(a, c) ā¤ b} = (3.9)
1 aā¤b
Observe that in all cases the inclusion relationship holds, namely R ā R. The
Ė
quality of the granulationādegranulation is expressed by comparing how much
Ė
the reconstruction of R given by (3.8), namely R, coincides with the relational
data themselves (R). The associated performance index expressing the quality of
reconstruction is deļ¬ned as follows:
N
Q= ā R(xk , yk , zk ) ā R(xk , yk , zk )
Ė 2
(3.10)
K=1
Again in light of the vector values assumed by the fuzzy relation, the above
distance is computed by taking distances over the successive coordinates of z.
For the Euclidean distance one explicitly speciļ¬es the components of the fuzzy
relation, which yields Q = ā p āN j=1 K=1 R(xk , yk , zk j ) ā R(xk , yk , zk j ) . The associ-
Ė 2
ated optimization process may then link with a formation of information granules
minimizing the distance expressed by (3.10). This can be taken into consideration
when designing fuzzy sets (membership functions) as well as selecting the number
of fuzzy sets. Referring to the original expression for the max-t composition, the cal-
culations are carried out coordinate-wise, that is we compute min(Cl (z1 ), R(x, y, z1 )),
min(Cl (z2 ), R(x, y, z2 )), etc.
13. 3 A Granular Description of Data: A Study in Evolvable Systems 69
In summary, the presented approach offers a general methodology of describing
data in the language of information granules, which can be schematically captured
as D ā G(D), where G denotes a granulation mechanism exploiting a collection
of information granules in the corresponding vocabularies. In view of ļ¬exibility
offered by different views at the data, we can highlight this effect in Fig. 3.7:
different granulation schemes cast an analysis of D in different perspectives and
give rise to different sequences of dominant granular descriptors.
3.7 Construction of Information Granules
Fuzzy clustering is a vehicle of information granulation. As such it can be used
here to form the elements of the codebook. As an example, we use here the well-
known method of the fuzzy c-means (FCM) [6]. The results of clustering come in
the form of the prototypes of the clusters and the partition matrix, which describes
an allocation of elements (membership degrees) to the clusters.
Let us discuss in detail on how the corresponding codebooks A, B, and C are
formed. For the spatial component, the data for the construction of elements of A
and B are one dimensional. The membership functions A1 , A2 , . . . , An are expressed
in the form
1
Ai (x) = 2/(Ī¶ ā1)
(3.11)
xāvi
ān
i=1 xāv j
i = 1, 2, . . . , n, where v1 , v2 , . . . , vn ā R are the prototypes constructed by running the
FCM using the data {x1 , x2 , . . . , xN }. The fuzziļ¬cation coefļ¬cient is denoted by Ī¶ ,
Ī¶ > 1. In the same way formed are the elements of the second codebook, B1 , B2 ,
. . . , Bm by clustering one-dimensional data {y1 , y2 , . . . , yN }. The codebook forming
the elements of C is constructed by clustering multidimensional data z1 , z2 , . . . , zN .
Here, the membership functions of the elements of C are expressed in the form
1
C1 (z) = 2/(Ī¶ ā1)
, (3.12)
zāvi
ār
i=1 zāvj
where v1 , v2 , . . . , vr are the prototypes located in [0, 1] p . The successive coordinates
of the membership function Cl , Cl (z) can be expressed explicitly by projecting the
prototypes onto the corresponding axes of the unit hypercube and computing the
membership functions based upon these projected prototypes, see Fig. 3.10.
Take the i0 -th coordinate of z, that is zi0 . Project the original prototypes
v1 , v2 , . . . , vc on this coordinate obtaining the real numbers v1i0 , v2i0 , . . . , vci0 .
By making use of these scalar prototypes, we obtain the membership functions
of the i0 -th coordinate of z.
14. 70 W. Pedrycz et al.
Fig. 3.10 Computing
membership values based on
i0 -th coordinate of the
prototypes
There are several strategies of organizing a clustering process; the three main
categories are worth highlighting here:
(a) Clustering realized separately for the spatial data and temporal data. In this
approach, we exercise two separate clustering algorithms, which do not interact.
The results are used together. The advantage of this design strategy is that one
can consider a number of well-established clustering techniques. A disadvan-
tage comes from a lack of interaction when forming the clusters.
(b) Clustering temporal and spatial data realized by running a single clustering
method. The interplay between the temporal and spatial data is realized by
forming an aggregate distance function composed of two parts quantifying
closeness of elements in the spatial domain and the space in which temporal
data are described. The approach by [15] is an example of the clustering strategy
falling within this realm. Further more advanced approaches in which the
distance functions are quite different are worth pursuing here (say, an Euclidean
distance used for spatial data and Itakura distance with regard to the temporal
data).
(c) Clustering temporal and spatial data realized by two clustering methods with a
granular interaction realized at the structural level of data. While the clustering
methods run individually, they receive feedback about the structure discovered
for the other data set (spatial or temporal). The interaction is typically realized at
the level of proximity matrices induced at the locally formed partition matrices
[13].
There are several parameters of the codebook formed by the information granules
that could be effectively used in the optimization of the description of the relational
data. This list includes:
(a) The number of information granules deļ¬ned for the corresponding spatial and
temporal coordinates.
(b) The values of the fuzziļ¬cation coefļ¬cients (Ī¶ ) used in the construction of
information granules. As discussed in the literature, by changing the value of
the fuzziļ¬cation coefļ¬cient, we directly affect the shape of the membership
function. The values close to 1 yield membership functions whose shape is
15. 3 A Granular Description of Data: A Study in Evolvable Systems 71
close to the characteristic functions of sets. The commonly used value of the
fuzziļ¬cation coefļ¬cient is equal to 2. Higher values of this parameter result in
spike-like membership functions.
The ļ¬rst group of the parameters is of combinatorial nature. In general, there is a
general monotonicity relationship: higher number of clusters can help reduce the
value of the optimization criterion (3.10). The optimization is of a combinatorial
nature, though so as to assure an effective design, one has to engage some techniques
of global optimization such as, e.g., evolutionary optimization. In light of the
monotonicity relationship, it could be more legitimate to look at the allocation
of the number of information granules to the vocabularies of the information
granules formed for the spatial and temporal dimensions of the variables. Let c
be a number of available information granules used in the granulation process
and speciļ¬ed in advance. We choose the number of granules for the x-, y-, and
temporal coordinates of the variables, say c1 , c2 , and c3 so that they sum is equal
to c. How to do this allocation, comes as a result of combinatorial optimization.
The parametric aspects of the optimization are implemented by selecting suitable
values of the fuzziļ¬cation coefļ¬cients; those could be assume different values for
the corresponding codebooks, say Ī¶1 for A, Ī¶2 for B and Ī¶3 for C.
3.8 Describing Relationships Between Granular Descriptors
of Data
The spatiotemporal data discussed so far along with their granular description
(signature) may be viewed as a certain snapshot of a certain phenomenon reported
over some period of time, say a week or a month. The data could be acquired over
consecutive time periods (time slices) thus forming a sequence of spatiotemporal
data D(T ), D(2T ), . . . D(MT ). As each data is subject to its granular description
(and behind each of these description comes a certain codebook), it is of interest
to look at the relationships between D(T ), D(2T ), etc. or better to say between
G(D(T )) and G(D(2T )). The latter task translates into a formation of relationships
between the components of the codebooks being used for the successive data
āslicesā say, D(T ), D(2T ), etc. Another way of expressing such dependencies is
to look at the characterization of information granules forming a certain vocabulary
at time slice T by the elements of the vocabulary pertaining to the successive 2T th
time slice. We discuss these two approaches in more detail.
3.8.1 Determination of Relationships Elements of Vocabularies
We construct the relationships between {Ai (2T ), B j (2T ),Ck (2T )} and Ai (T ), B j (T ),
Ck (T ), so the evolution of the underlying system is expressed in terms of evolution
16. 72 W. Pedrycz et al.
Fig. 3.11 Computing the
degrees of activation of the
elements of the codebook
A(2T ) by the elements of
A(T ); vi (T ) and v j (2T )
(black and grey dots) are the
prototypes of the
corresponding fuzzy sets in
the T and 2T time periods
of information granules describing the data structure. The number of elements in
the vocabularies Ai (T ) and Ai (2T ) need not to be same as the granularity of the
vocabularies can evolve to reļ¬ect the complexity of the data and structures being
encountered. The relationships between Ai (T ) and Al (2T ) can be expressed in terms
of logic dependencies. In general, we formulate a logic relationship in the form of
the fuzzy relational equation:
a(2T ) = a(T ) ā¦ L(T, 2T ) (3.13)
where ā¦ stands for a certain composition operator, say a max-t or a maxāmin
one (being already used in the previous constructs) and L(T, 2T ) captures the
relationships between coordinates of the vectors a(T ) and a(2T ). The vectors
a(T ) and a(2T ) being the elements of the unit hypercubes, contain the degrees of
activation of the elements of the vocabulary A. The dimensionality of a(T ) and
a(2T ) is equal to n(T ) and n(2T ), respectively. As noted, the dimensionality of
these two vectors might not be the same. The estimation of the fuzzy relation can
be realized in a number of different ways depending upon a formation of the inputā
output data to be used in the estimation procedure of L(T, 2T ). We note that the
above relational description is a nonstationary model as the fuzzy relation L depends
on the time indexes (T and 2T ). One could form a stationary model where L is time
independent that is we have a(nT ) = a(nT ā T ) ā¦ L, n = 2, 3, . . . .
A certain way, using which this could be realized in an efļ¬cient way is to consider
the data composed of the pairs (here we are considering the elements of the ļ¬rst
codebook, A)
a1 (T ) = [1 0 . . . 0] a1 (2T ) = [Ī»11 Ī»12 . . . Ī»1n(2T ) ] = Ī»1 (3.14)
Here Ī»1 (T ) is a degree of activation of the vocabulary at 2T by the 1st element of
the vocabulary used at T . The degrees of activation Ī»1i are computed as follows:
1
Ī»1i = 2/(Ī¶ ā1)
(3.15)
n(2T ) v1 (T )āvi (2T )
ā j=1 v1 (T )āv j (2T )
Refer to Fig. 3.11 for a more detailed explanation.
17. 3 A Granular Description of Data: A Study in Evolvable Systems 73
Likewise, we have the remaining n(T ) inputāoutput pairs
a2 (T ) = [0 1 . . . 0] a2 (2T ) = [Ī»21 Ī»22 . . . Ī»2n(2T ) ] = Ī»2 (3.16)
an(T ) (T ) = [0 0 . . . 1] an(T ) (2T ) = [Ī»n(T )1 Ī»n(T )2 . . . Ī»n(T )n(2T ) ] = Ī»2 (3.17)
We consider all pairs of data {(a1 (T ), a1 (2T )), . . . , (an(T ) (T ), an(T ) (2T )} and solve
a system of relational equations (3.13) with regard to L(T, 2T ). In case of the max-t
composition, the result is straightforward; the solution does exist and the largest one
is expressed in the form
L(T, 2T ) = [1 0 . . . 0]T Ī¦ [Ī»11 Ī»12 . . . Ī»1n(2T) ]
Ė [0 1 . . . 0]Ī¦ [Ī»21 Ī»22 . . . Ī»2n(2T ) ]
Ć ... [0 0 . . . 1]Ī¦ [Ī»n(T )1 Ī»n(T )2 . . . Ī»n(T )n(2T ) ] (3.18)
In other words, the fuzzy relation computed in this way comes in the form
L(T, 2T ) = |Ī»1 Ī»2 . . . Ī»n(T ) |T (3.19)
We can quantify the changes when moving from one vocabulary to the next one by
expressing uncertainty of expressing the elements of the codebook at 2T , A(2T ) in
terms of the elements of the codebook formed for the data at T .
3.8.2 Uncertainty Quantiļ¬cation of Variable Codebooks
We use the entropy measure [9] to quantify the level of uncertainty. More speciļ¬-
cally, we look at the successive rows of L(T, 2T ) and for each of them compute the
entropy. For instance, for the i-th row we have
n(2T )
H Ī»i = ā ā Ī»i j log2 (Ī»i j ) (3.20)
j=1
The highest uncertainty (viz. difļ¬culty of expressing the elements of one codebook
by another one) occurs when all elements of i are the same and equal to 1/n(2T ).
n(T )
At a more aggregate level, we consider an average entropy that is n(T ) āi=1 H(Ī»i ).
1
Its value is reļ¬ective of the level of variability between the codebooks used in
successive time epochs and uncertainty when expressing the elements of A(T ) in
terms of the components of A(2T ). In the same way, we compute the entropy
associated with the evolution of the codebooks B and C. This type of evaluation
is useful in the assessment of the evolution of the granularity of the descriptors of
data. The granular description of the system presented in this way highlights and
quantiļ¬es two important facets of evolvability, that is evolvability being reļ¬ective
of the changes of the system itself and evolvability being an immediate result
18. 74 W. Pedrycz et al.
of the adjustable perception of the data caused directly by changes made to the
vocabularies used in the description of the data. These two are interlinked.
In case we ļ¬x our perception of the system that is the codebooks are left
unchanged A(T ) = A(2T ) = . . . B(T ) = B(2T ) = . . . , etc., the matrices of granular
descriptors of the data, G(T ), G(2T ), . . . , etc. are essentially reļ¬ective of the
dynamics of the system. The dynamics being captured in this way depends upon
a level of speciļ¬city of the codebooks. What is intuitive, the less detailed the
codebooks, the closer the granular descriptors become and some minor evolvability
trends present in the system are not taken into consideration. When the codebook
(vocabulary) itself changes (because of the structural complexity of the data
themselves or perception at the system, which has been changed), we envision an
aggregate effect of evolvability stemming from the dynamics of the system as well
as its varying level of perception.
3.9 Conclusions
There is an ongoing quest in data analysis to make the results more user friendly
(interpretable) as well as make the role of the user more proactive in the pro-
cesses of data analysis. The study offered in this paper arises as one among
interesting possibilities with this regard. It should be noted that the closeness
among data is expressed by taking into account a certain aggregate of closeness
in the spatial domain and temporal domain. While the xāy coordinates could
be left unchanged in successive time slices (which reļ¬ects a situation of ļ¬xed
measuring stations/sensors), one can easily handle a situation of mobile sensors
whose distribution varies over time slices.
It has been demonstrated that the concept of information granularity not only
helps conceptualize a view at the data but also construct effective algorithms
supporting the development of relational models.
The nonstationarity phenomenon might be inherent to the data themselves (viz.
the system or phenomenon producing the data). The evolution effect could be
also related to the varying position one assumes when studying the system. These
two facets of dynamical manifestation of the system/perception are captured and
quantiļ¬ed in terms of the relational descriptors and dependencies between them.
Acknowledgments Support from Natural Sciences and Engineering Research Council (NSERC)
is greatly appreciated.
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