The document is a draft project report for detecting spatial outliers in spatial datasets. It was written by Shan Huang and Jisu Oh for their Csci 8715 course at the University of Minnesota. The project aims to build a new class to detect spatial outliers in WEKA, a machine learning software, by developing an algorithm that compares attribute values of spatially referenced objects to their neighbors. The report outlines the motivation, related work, problem statement, implementation details including the algorithm and user interface, methodology, contributions, conclusions and future work.
This document compares four clustering algorithms (K-means, hierarchical, EM, and density-based) using the WEKA tool. It applies the algorithms to a dataset of software classes and evaluates them based on number of clusters, time to build models, squared errors, and log likelihood. The results show that K-means performs best in terms of time to build models, while density-based clustering performs best in terms of log likelihood. Overall, the document concludes that K-means is the best algorithm for this dataset because it balances low runtime and good clustering accuracy.
An Efficient Unsupervised AdaptiveAntihub Technique for Outlier Detection in ...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Architectural decisions in designing data and computation intensive systems can have a major impact on the ability of these systems to perform statistical and other complex calculations efficiently. The storage, processing, tools, and associated databases coupled with the networking and compute infrastructure make some kinds of computations easier, and other harder. This talk will provide an introduction to software and data systems components that are important for understanding how these choices impact data analysis uncertainties and costs, and thus for developing system and software designs best suited to statistical analyses.
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.
Detection of Outliers in Large Dataset using Distributed ApproachEditor IJMTER
This document discusses a distributed approach for detecting outliers in large datasets. It introduces an algorithm based on the concept of an outlier detection solving set, which is a small subset of the dataset that can predict outliers. The algorithm exploits parallel computation to achieve significant time savings over traditional nested loop approaches. Experimental results show the algorithm scales well to increasing numbers of nodes. A variant is also discussed that reduces the amount of data transferred, improving communication costs and runtime. The solving set computed in a distributed environment has the same quality as that produced by the corresponding centralized method.
This document discusses techniques for analyzing unstructured text data from computer data inspection. It discusses using clustering algorithms like K-means and hierarchical clustering to automatically group related documents without supervision. The goal is to help computer examiners analyze large amounts of text data more efficiently. Prior work on clustering ensembles, evolving gene expression clusters, self-organizing maps, and thematically clustering search results is reviewed as relevant to this problem. The problem is how to identify and cluster documents stored across multiple remote locations during computer inspections when existing algorithms make this difficult.
The document discusses different clustering algorithms, including k-means and EM clustering. K-means aims to partition items into k clusters such that each item belongs to the cluster with the nearest mean. It works iteratively to assign items to centroids and recompute centroids until the clusters no longer change. EM clustering generalizes k-means by computing probabilities of cluster membership based on probability distributions, with the goal of maximizing the overall probability of items given the clusters. Both algorithms are used to group similar items in applications like market segmentation.
This document discusses anomaly detection techniques. It defines anomaly detection as the identification of items, events or observations that do not conform to expected patterns in data mining. It then covers various anomaly detection methods including unsupervised, supervised and semi-supervised techniques. Specific algorithms discussed include LOF, RNN, and Twitter's Seasonal Hybrid ESD approach. Real-world applications of anomaly detection are also mentioned such as intrusion detection, fraud detection and system health monitoring.
This document compares four clustering algorithms (K-means, hierarchical, EM, and density-based) using the WEKA tool. It applies the algorithms to a dataset of software classes and evaluates them based on number of clusters, time to build models, squared errors, and log likelihood. The results show that K-means performs best in terms of time to build models, while density-based clustering performs best in terms of log likelihood. Overall, the document concludes that K-means is the best algorithm for this dataset because it balances low runtime and good clustering accuracy.
An Efficient Unsupervised AdaptiveAntihub Technique for Outlier Detection in ...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Architectural decisions in designing data and computation intensive systems can have a major impact on the ability of these systems to perform statistical and other complex calculations efficiently. The storage, processing, tools, and associated databases coupled with the networking and compute infrastructure make some kinds of computations easier, and other harder. This talk will provide an introduction to software and data systems components that are important for understanding how these choices impact data analysis uncertainties and costs, and thus for developing system and software designs best suited to statistical analyses.
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.
Detection of Outliers in Large Dataset using Distributed ApproachEditor IJMTER
This document discusses a distributed approach for detecting outliers in large datasets. It introduces an algorithm based on the concept of an outlier detection solving set, which is a small subset of the dataset that can predict outliers. The algorithm exploits parallel computation to achieve significant time savings over traditional nested loop approaches. Experimental results show the algorithm scales well to increasing numbers of nodes. A variant is also discussed that reduces the amount of data transferred, improving communication costs and runtime. The solving set computed in a distributed environment has the same quality as that produced by the corresponding centralized method.
This document discusses techniques for analyzing unstructured text data from computer data inspection. It discusses using clustering algorithms like K-means and hierarchical clustering to automatically group related documents without supervision. The goal is to help computer examiners analyze large amounts of text data more efficiently. Prior work on clustering ensembles, evolving gene expression clusters, self-organizing maps, and thematically clustering search results is reviewed as relevant to this problem. The problem is how to identify and cluster documents stored across multiple remote locations during computer inspections when existing algorithms make this difficult.
The document discusses different clustering algorithms, including k-means and EM clustering. K-means aims to partition items into k clusters such that each item belongs to the cluster with the nearest mean. It works iteratively to assign items to centroids and recompute centroids until the clusters no longer change. EM clustering generalizes k-means by computing probabilities of cluster membership based on probability distributions, with the goal of maximizing the overall probability of items given the clusters. Both algorithms are used to group similar items in applications like market segmentation.
This document discusses anomaly detection techniques. It defines anomaly detection as the identification of items, events or observations that do not conform to expected patterns in data mining. It then covers various anomaly detection methods including unsupervised, supervised and semi-supervised techniques. Specific algorithms discussed include LOF, RNN, and Twitter's Seasonal Hybrid ESD approach. Real-world applications of anomaly detection are also mentioned such as intrusion detection, fraud detection and system health monitoring.
Exploiting Hierarchical Context on a Large Database of Object Categories Debaleena Chattopadhyay
This document summarizes a paper that presents a tree-structured context model to exploit hierarchical context on a large database of object categories. The model incorporates co-occurrence statistics, spatial relationships between objects, and global/local image features. It was trained and evaluated on the SUN 09 dataset containing over 12,000 images across 200 object categories. Results showed the context model improved object recognition performance on PASCAL 07 and achieved high accuracy on image annotation and detecting out-of-context objects in SUN 09 scenes.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
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.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Outlier Detection Approaches in Data MiningIRJET Journal
This document discusses different approaches for outlier detection in data mining. It begins by defining outliers and describing the importance of outlier detection. It then reviews previous work on outlier detection, which includes statistical, distance-based, deviation-based, and density-based approaches for classic outlier detection, as well as space-based and graph-based approaches for spatial outlier detection. The document goes on to describe classic and spatial outlier detection approaches in more detail. It concludes by discussing some recent advancements in outlier detection techniques like SLOF and non-parametric composite outlier detection.
This document discusses the development of a scalable neural network platform for predictive metabonomics. It aims to create a "white box" neural network model that allows users full control over the network architecture. Particle swarm optimization will be used to train the network. The implementation uses C++ and OpenNN libraries in Visual Studio. Future work includes applying neural networks to other applications like structure activity relationships and instrument optimization, and creating a graphical user interface.
Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of
sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data.
Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have
been proposed to overcome the problem of outlier detection in spatial Geographic data. In this paper an
efficient clustering and density based outlier detection framework has been proposed. The process of
outlier detection has been categorized into two steps in the first step data has been clustered together based
on any density based DBSCAN algorithm and in the second stage outlier detection is performed using LOF.
The purpose is to perform clustering and outlier mining simultaneously to improve feasibility of framework.
To verify the efficiency and robustness of proposed method, comparative study of proposed approach and
several existing approaches are presented in detail, various simulation results demonstrate the
effectiveness of the proposed approach.
Adaptive and online one class support vector machine-based outlier detectionNguyen Duong
This document proposes three adaptive and online one-class support vector machine techniques for outlier detection in wireless sensor networks. The techniques sequentially update the model of normal sensor data behavior and take advantage of spatial and temporal correlations between sensor readings to identify outliers with high accuracy while minimizing network resource usage. Experiments on both synthetic and real wireless sensor network data show that the proposed online outlier detection techniques achieve better detection accuracy and lower false alarm rates than previous techniques.
MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace DataIRJET Journal
The document describes a new algorithm called MPSKM that clusters uneven dimensional time series subspace data. The algorithm aims to select attribute ranks based on their involvement in the data set and identify global and local patterns. It automates determining the number of clusters and cluster centers. The algorithm calculates a rank matrix based on the sum of squared errors between attribute pairs to rank attributes. It then uses the ranks to transform the data dimensions before clustering. The algorithm is tested on weather data and shown to reduce iteration counts and error compared to traditional methods.
The document discusses improving neural network classification of astronomical objects into stars and galaxies. It analyzes the classifier used in the SExtractor software, which uses a multi-layer perceptron neural network trained on simulated data. The authors build their own classifier using WEKA to automatically select features and the neural network topology from real data classified by an expert. Their classifier achieved slightly better results than SExtractor and used fewer computational resources. However, more domain specific information is still needed to build a better star/galaxy separator.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Data mining techniques application for prediction in OLAP cubeIJECEIAES
Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.
This document describes an automated clustering and outlier detection program. The program normalizes data, performs principal component analysis to select important components, compares clustering algorithms, selects the best model using silhouette values, and produces outputs labeling clusters and outliers. It is demonstrated on a sample of 5,000 credit card customer records, identifying a small cluster of 3 accounts as outliers based on features like new status and high late payments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
A New Extraction Optimization Approach to Frequent 2 Item setsijcsa
In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1- itemets supports (cardinal 1 sets), then we prune the 1-itemsets not frequent and keep only those that are frequent (ie those with the item sets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAXMINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of sideby-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Exploiting Hierarchical Context on a Large Database of Object Categories Debaleena Chattopadhyay
This document summarizes a paper that presents a tree-structured context model to exploit hierarchical context on a large database of object categories. The model incorporates co-occurrence statistics, spatial relationships between objects, and global/local image features. It was trained and evaluated on the SUN 09 dataset containing over 12,000 images across 200 object categories. Results showed the context model improved object recognition performance on PASCAL 07 and achieved high accuracy on image annotation and detecting out-of-context objects in SUN 09 scenes.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
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.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Outlier Detection Approaches in Data MiningIRJET Journal
This document discusses different approaches for outlier detection in data mining. It begins by defining outliers and describing the importance of outlier detection. It then reviews previous work on outlier detection, which includes statistical, distance-based, deviation-based, and density-based approaches for classic outlier detection, as well as space-based and graph-based approaches for spatial outlier detection. The document goes on to describe classic and spatial outlier detection approaches in more detail. It concludes by discussing some recent advancements in outlier detection techniques like SLOF and non-parametric composite outlier detection.
This document discusses the development of a scalable neural network platform for predictive metabonomics. It aims to create a "white box" neural network model that allows users full control over the network architecture. Particle swarm optimization will be used to train the network. The implementation uses C++ and OpenNN libraries in Visual Studio. Future work includes applying neural networks to other applications like structure activity relationships and instrument optimization, and creating a graphical user interface.
Outlier detection is very interesting, useful and challenging problem in the field of data mining. Because of
sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data.
Problem of finding irregular feature in spatial data need to be explore. Many existing approaches have
been proposed to overcome the problem of outlier detection in spatial Geographic data. In this paper an
efficient clustering and density based outlier detection framework has been proposed. The process of
outlier detection has been categorized into two steps in the first step data has been clustered together based
on any density based DBSCAN algorithm and in the second stage outlier detection is performed using LOF.
The purpose is to perform clustering and outlier mining simultaneously to improve feasibility of framework.
To verify the efficiency and robustness of proposed method, comparative study of proposed approach and
several existing approaches are presented in detail, various simulation results demonstrate the
effectiveness of the proposed approach.
Adaptive and online one class support vector machine-based outlier detectionNguyen Duong
This document proposes three adaptive and online one-class support vector machine techniques for outlier detection in wireless sensor networks. The techniques sequentially update the model of normal sensor data behavior and take advantage of spatial and temporal correlations between sensor readings to identify outliers with high accuracy while minimizing network resource usage. Experiments on both synthetic and real wireless sensor network data show that the proposed online outlier detection techniques achieve better detection accuracy and lower false alarm rates than previous techniques.
MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace DataIRJET Journal
The document describes a new algorithm called MPSKM that clusters uneven dimensional time series subspace data. The algorithm aims to select attribute ranks based on their involvement in the data set and identify global and local patterns. It automates determining the number of clusters and cluster centers. The algorithm calculates a rank matrix based on the sum of squared errors between attribute pairs to rank attributes. It then uses the ranks to transform the data dimensions before clustering. The algorithm is tested on weather data and shown to reduce iteration counts and error compared to traditional methods.
The document discusses improving neural network classification of astronomical objects into stars and galaxies. It analyzes the classifier used in the SExtractor software, which uses a multi-layer perceptron neural network trained on simulated data. The authors build their own classifier using WEKA to automatically select features and the neural network topology from real data classified by an expert. Their classifier achieved slightly better results than SExtractor and used fewer computational resources. However, more domain specific information is still needed to build a better star/galaxy separator.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Data mining techniques application for prediction in OLAP cubeIJECEIAES
Data warehouses represent collections of data organized to support a process of decision support, and provide an appropriate solution for managing large volumes of data. OLAP online analytics is a technology that complements data warehouses to make data usable and understandable by users, by providing tools for visualization, exploration, and navigation of data-cubes. On the other hand, data mining allows the extraction of knowledge from data with different methods of description, classification, explanation and prediction. As part of this work, we propose new ways to improve existing approaches in the process of decision support. In the continuity of the work treating the coupling between the online analysis and data mining to integrate prediction into OLAP, an approach based on automatic learning with Clustering is proposed in order to partition an initial data cube into dense sub-cubes that could serve as a learning set to build a prediction model. The technique of data mining by regression trees is then applied for each sub-cube to predict the value of a cell.
This document describes an automated clustering and outlier detection program. The program normalizes data, performs principal component analysis to select important components, compares clustering algorithms, selects the best model using silhouette values, and produces outputs labeling clusters and outliers. It is demonstrated on a sample of 5,000 credit card customer records, identifying a small cluster of 3 accounts as outliers based on features like new status and high late payments.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
A New Extraction Optimization Approach to Frequent 2 Item setsijcsa
In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1- itemets supports (cardinal 1 sets), then we prune the 1-itemsets not frequent and keep only those that are frequent (ie those with the item sets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAXMINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of sideby-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document summarizes a research project that uses data mining classification techniques to analyze a trajectory dataset in order to predict a user's mode of transportation.
2) Several classification algorithms (decision tree, naive Bayes, Bayesian network, neural network, support vector machines) were evaluated using metrics like accuracy, recall, precision, and kappa. The results showed that decision trees and Bayesian networks performed best.
3) Future work proposed applying density-based clustering to identify dense regions and build prediction models for public vs. personal transportation use in those areas based on historical data.
Integrated Hidden Markov Model and Kalman Filter for Online Object Trackingijsrd.com
Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.
1. The document summarizes ongoing data mining and machine learning research at the University of Houston from 2006-2009.
2. Key areas of research included developing shape-aware clustering algorithms, discovering regional knowledge in geo-referenced datasets, emergent pattern discovery, and various machine learning applications.
3. The researchers were developing techniques for clustering with plug-in fitness functions, discovering spatial risk patterns like arsenic levels, and an open source data mining framework called Cougar2.
International Journal of Engineering Research and DevelopmentIJERD Editor
The document provides a survey of research on sensor association rules for mining behavioral patterns from wireless sensor network data. Sensor association rules aim to discover temporal relationships between sensor nodes by detecting correlated events. Various approaches are discussed, including techniques for distributed in-network mining, handling data streams, reducing redundancy, and applying association rules to applications like missing data estimation. Overall, the survey finds that sensor association rules are an effective knowledge discovery technique for wireless sensor networks.
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
Object tracking with SURF: ARM-Based platform ImplementationEditor IJCATR
This document describes research on implementing the SURF (Speeded Up Robust Features) algorithm for real-time object tracking on a Raspberry Pi mobile platform. The SURF algorithm extracts features from images that can be used for object detection and tracking across multiple frames. The researchers implemented an application on the Raspberry Pi to select an object image and perform SURF feature extraction and matching between the object image and live camera frames to detect and track the object in real-time video. They discuss adapting the SURF algorithm and libraries to optimize performance on the Raspberry Pi's hardware within its computational limitations for real-time tracking. Experimental results demonstrate object detection and tracking on test and live video streams using the Raspberry
SVM Based Identification of Psychological Personality Using Handwritten Text IJERA Editor
This document describes a study that uses handwriting analysis to identify psychological personality traits using support vector machines (SVM). Handwriting samples were collected and preprocessed by removing noise and segmenting lines. Features like slope, shape, and edge histograms were extracted. SVM with radial basis function kernel was used for classification. Analysis of single lines achieved 95% accuracy while multiple lines achieved 91% accuracy in identifying traits like cheerfulness and weariness. The methodology was also applied to analyze handwriting of celebrities and compare the results to analyses by graphologists. The study aims to automate handwriting analysis using machine learning techniques.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
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.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
This document summarizes an experiment that evaluated the efficiency of three search strategies for autonomous rendezvous in space: random, semi-autonomous, and autonomous. The experiment used LEGO robots to simulate a space tug searching for a target in a two-dimensional space, and measured the time and energy required for each strategy. It was found that the semi-autonomous strategy was most energy efficient but most time-consuming, while the autonomous strategy proved most suitable for space applications by being both reasonably energy efficient and faster. The results provide insight into optimal search algorithms for an actual space tug vehicle to implement during rendezvous and docking.
MAXIMUM CORRENTROPY BASED DICTIONARY LEARNING FOR PHYSICAL ACTIVITY RECOGNITI...sherinmm
This document proposes a correntropy induced dictionary pair learning framework for physical activity recognition using wearable sensors. It begins with an introduction to physical activity recognition and related work. It then presents the proposed methodology, which consists of two stages: data processing and recognition. The recognition stage involves jointly learning a synthesis dictionary and analysis dictionary based on the maximum correntropy criterion. This is done using an alternating direction method of multipliers combined with an iteratively reweighted method to solve the non-convex objective function. The framework is validated on physical activity recognition and intensity estimation tasks using a publicly available dataset. Experimental results show the correntropy induced dictionary learning approach achieves high accuracy using simple features and is competitive with other methods requiring prior knowledge
Maximum Correntropy Based Dictionary Learning Framework for Physical Activity...sherinmm
Due to its symbolic role in ubiquitous health monitoring,
physical activity recognition with wearable body sensors has been in the
limelight in both research and industrial communities. Physical activity
recognition is difficult due to the inherent complexity involved with different
walking styles and human body movements. Thus we present a
correntropy induced dictionary pair learning framework to achieve this
recognition. Our algorithm for this framework jointly learns a synthesis
dictionary and an analysis dictionary in order to simultaneously perform
signal representation and classification once the time-domain features
have been extracted. In particular, the dictionary pair learning algorithm
is developed based on the maximum correntropy criterion, which
is much more insensitive to outliers. In order to develop a more tractable
and practical approach, we employ a combination of alternating direction
method of multipliers and an iteratively reweighted method to approximately
minimize the objective function. We validate the effectiveness of
our proposed model by employing it on an activity recognition problem
and an intensity estimation problem, both of which include a large number
of physical activities from the recently released PAMAP2 dataset.
Experimental results indicate that classifiers built using this correntropy
induced dictionary learning based framework achieve high accuracy by
using simple features, and that this approach gives results competitive
with classical systems built upon features with prior knowledge.
An overlapping conscious relief-based feature subset selection methodIJECEIAES
Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods.
EMBC'13 Poster Presentation on "A Bio-Inspired Cooperative Algorithm for Dist...Md Kafiul Islam
The document proposes an algorithm for distributed optimization with mobile nodes that do not know the cost function beforehand. Each node estimates the gradient vector to update its location. The proposed algorithm improves upon an existing algorithm by relying on information-rich nodes in the neighborhood instead of a linear combination of neighbors' estimates. It also uses a variable step size to increase the probability of finding information-rich nodes early in the iterations. Simulation results show the proposed algorithm achieves better performance than the existing algorithm and a non-cooperative scheme. The algorithm has applications in sensor networks, environmental monitoring, and other domains.
This document discusses using artificial neural networks for fault detection and diagnosis of power systems. It presents neural networks as a suitable approach for power system fault diagnosis due to their ability to generalize, learn online, and operate in real-time. The document describes a case study where a neural network was trained on data from a model power system to detect and diagnose 53 different fault types based on relay and circuit breaker status inputs. The neural network was able to accurately diagnose faults, distinguish between single and multiple faults, and showed graceful degradation when diagnosis was imperfect.
Combined cosine-linear regression model similarity with application to handwr...IJECEIAES
This document presents a combined cosine-linear regression model for calculating similarity between handwritten word images. It first provides an overview of various commonly used similarity and distance measures such as Euclidean, Manhattan, Minkowski, Cosine, Jaccard, and Chebyshev distances. It then compares the performance of these measures on a handwritten Arabic document dataset, finding that cosine distance performs best. However, cosine distance is affected by the size of the visual codebook used. The document proposes a floating threshold based on a linear regression model that considers both the codebook size and number of image features, in order to better measure similarity between word images. Experiments on a historical Arabic document collection demonstrate the effectiveness of this combined cosine-linear regression
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
Vicki Haugen McMaster is seeking a position in web design, front-end development, or digital photography. She has over 12 years of experience in front-end development using HTML and CSS, as well as expertise in Adobe Creative Suite programs like Photoshop. Her previous roles include web developer positions at Aquent and The Creative Group where she updated websites and assisted development teams.
Kyril Mossin is a web designer and Flash programmer with over 15 years of experience in graphic design, web development, and multimedia production. He has created websites for advertising agencies, photo agencies, and other clients. His skills include Flash, ActionScript, Photoshop, Dreamweaver, and HTML. He has a background in print production and technical writing.
Chai Riphenburg is a graphic designer and web developer based in Hesperia, CA with over 20 years of experience in design, development, and management. They have worked for companies large and small, from startups to major corporations, focusing on visual design, interaction design, and production design for websites, games, and software. Their ideal compensation range is $85k-$100k annually. They maintain an online portfolio at http://www.37design.net/chai and can be contacted at 37design@gmail.com or 760-244-0293.
This document contains contact information and a summary of qualifications for Michael Almond, a web professional with over 8 years of experience in user experience design and front-end development. It includes details on his work history at various companies focusing on visual design, information architecture, and user experience best practices. It also lists education and a partial client list.
This document outlines the terms of a website design contract between a client and PrimeWeb, including details of standard website packages, additional fees, payment schedules, copyright terms, and responsibilities of both parties. It specifies elements included in standard website packages, hourly rates, completion targets, and policies regarding initial payments and refunds. The client and PrimeWeb must both sign to agree to the terms laid out in the 16 clauses.
This document is a design proposal form from Web Design by Kimberly for creating a website. It outlines standard website packages starting at $250 for up to 5 pages. Additional services like hosting, domain registration, forms, updates and shopping cart configuration are available for extra fees. The form requests information from the client like business details, site specifics and graphics to begin the design process.
This document is a resume for Amber Hansford seeking a position in web design. It summarizes her skills and experience, which include over 6 years of graphic and web design experience using programs like Photoshop, Illustrator, and Dreamweaver. She has experience designing websites for various clients, making them accessible, and coding them by hand in HTML. Her education includes a Bachelor's in Visual Communications and she is certified in various design programs and concepts. Links to examples of her accessible, compliant work are provided.
1. Name: Jisu Oh, Shan Huang
Date : April 12, 2004
Course : Csci 8715
Professor : Shashi Shekhar
Project Report (draft version)
“Spatial Outlier Detection”
Shan Huang, Jisu Oh
Computer Science Department, University of Minnesota, 200 Union Street SE,
Minneapolis, MN 55455, U.S.A
E-mail: shahuang@cs.umn.edu, joh@cs.umn.edu
http://www-users.cs.umn.edu/~joh/csci8715/HW-list.htm
1. Introduction
A spatial outlier is a spatially referenced object whose non-spatial attribute values are
significantly different from the values of its neighborhood. Identification of spatial
outliers can lead to the discovery of unexpected, interesting, and useful spatial
patterns for further analysis. WEKA is a collection of machine learning algorithms
for solving real-world data mining problems. It is written in Java and runs on almost
any platform. Basic data mining functions as well as regression, association rules and
clustering algorithms have also been implemented in WEKA, but their algorithms can
only operate on traditional non-spatial database. The purpose of this project is to
build a new class, which can detect spatial outlier in a spatial data set.
2. Motivation
Machine learning/data mining discovers new things or structure that is unknown to
humans. It enables a computer program to automatically analyze large-scale data and
decide what information is most important. We can then use this information to make
predictions or to make decisions faster and more accurately.
1
2. Many organizations rely on spatial analysis to make business and agency decisions
and to conduct research. The main difference between data mining in relational DBS
and in spatial DBS is the interest of neighboring object’s attributes may have an
influence on the current object, so the neighboring object have to be considered as
well. The explicit location and extension of spatial objects define implicit relations of
spatial neighborhood which are used by spatial data mining algorithms. Therefore,
new techniques are required for effective and efficient data mining.
WEKA is a collection of machine learning algorithms for solving real-world data
mining problems. It is written in Java and runs on almost any platform. Basic data
mining functions as well as regression, association rules and clustering algorithms
have also been implemented in WEKA, but these algorithms can only operate on
traditional non-spatial database.
The aim of this project is to build new classes and algorithm which can handle spatial
data, such as spatial regression, spatial association rule (co-location), and spatial
outlier detection.
2. Related works
Detecting spatial outliers is useful in many applications of geographic information
systems, including transportation, ecology, public safety, public health, climatology,
and location based services [2].
Shekhar et al. introduced a method for detecting spatial outliers in graph data set
based on the distribution property of the difference between an attribute value and the
average attribute value of its neighbors [3]. Shekhar also proposed an algorithm to
find all outliers in a dataset, which replace many statistical discordance tests,
regardless of any knowledge about the underlying distribution of the attributes [7].
Stephen D. Bay et al. introduced a simple nested loop algorithm to detect spatial
2
3. outlier, which gives linear time performance when data is in random order and a
simple pruning rule is used [4]. Existing methods for finding outliers can only deal
efficiently with two dimensions/attributes of a dataset.
A distance-based detection method was introduced by Sridhar Ramaswamy et al.,
which ranks each point on the basis of its distance to its kth nearest neighbor and
declares the top n points in this ranking to outliers. A highly efficient partition-based
algorithm was also introduced in this paper [6]. Edwin M. Knorr et al. proposed
another distance-base outlier detection method that can be done efficiently for large
datasets, and for k-dimensional datasets with large value of k [9]. Spatial outliers are
most time represented as point data, but they are frequently represented in region, i.e.,
a group of point. Jiang Zhao et al. proposed a wavelet analysis based approach to
detect region outlier [5].
Markus M. Breunig et al. showed a different approach to detecting spatial outliers; it
was done by assigning to each object a degree of being an outlier, the degree, which
was called the local outlier factor of an object, depends on how isolated the object is
with respect to the surrounding neighborhood [10].
Currently, there are many spatial statictis software available. S-PLUS spatial
statistics are the first comprehensive, object-oriented software package for the
analysis of spatial data. It includes a fairly wide range of techniques for spatial data
analysis.
R is a language similar to S for statistical data analysis, based on modern
programming concepts and released under the GNU General Public License. It
follows a broad outline of existing collections of functions for spatial statistics written
for S. Functions for three types of spatial statistics are covered: spatially continuous
data, point pattern data, and area data.
SAS is another powerful analytical and reporting system. The SAS Bridge for ESRI
provides a new way to exchange spatial attribute data between ArcGIS, the market
3
4. leading geographic information system (GIS) software from ESRI, and SAS. This
new product links spatial, numeric and textual data through a single interface to
improve efficiency, produce more intelligent results and communicate those results
more effectively.
3. Problem Statement
The input data set using in this project were collected from the sensor stations
embedded in Interstate highways surrounding the Twin Cities area in Minnesota, US.
Each station measures the traffic volume and occupancy on a particular stretch of the
highway at 5-min intervals. Each data set consists of 288 rows of the 5-min detector
records, starting from 0:0 AM; each row contains 300 tuples of (volume, occupancy)
for 150 stations; each tuple in the row represents the traffic volume and occupancy of
the detector within the 5-min period. The neighbor is defined in terms of topological
rather than Euclidean distance. Our objective is to determine stations that are
“outliers” based on the volumes of the traffic measurements from each station.
A spatial outlier is a spatially referenced object whose non-spatial attribute values are
significantly different from those of other spatially referenced objects in its spatial
neighborhood. In this application, the outlier would be the one station which detects
a very high volume compare to the neighboring station. For instance, at 1:00 AM,
station A detects a volume of 250, which the two neighbor stations B and C only
collect single digits volume, then in this case station A would be considered as an
local outlier.
The algorithm used in this project was proposed in the paper “A Unified Approach to
Detecting Spatial Outliers”.[7] The location is compared to its neighborhood using
the function:
S(x) = [ f ( x ) − Εy ∈ N(x)(f(y))], where
f(x) - attribute value for a location x
N(x) - set of neighbors of x
4
5. Ey∈ N(x)(f(y)) - average attribute value for the neighbors of x
S(x) – difference of the attribute value of a sensor located at x and the average
attribute value of x’s neighbors.
Spatial statistic is used for detecting spatial outliers for normally distributed f(x).
s ( x ) − µs
Zs(x) = 〉θ
σs
µs - Mean value of S(x)
σs - Standard deviation of S(x)
θ - Specified confidence level
4. Implementation
4.1 Algorithm
The algorithm is divided into two subparts, (1) Model construction (2) Outlier detection.
The first part of the algorithm is finding Ey∈ N(x)(f(y) (E(x)), the average attributes
value for the neighbors of x. For each station, its two neighbor stations are retrieved, and
the average of neighbor station’s volume is computed. The second part of the algorithm,
for each iteration one outlier is detected. First, the standard deviation and the average for
the all the E(x) is computed, then for each station using f(x) – E(x) to find the S(x), which
S(x) is the function that compares a station with its neighborhood. Lastly, the spatial
s ( x ) − µs
statistics Zs(x) = 〉θ are computed and compare to θ , user specified value. In
σs
the outlier detection program, it means 68%, 95%, or 99% confidence interval. Once one
outlier is identified, its original value is replace with the average value of its
neighborhood, and the algorithm will starts over again to second outlier, and so on. In
this algorithm, the number of outliers are detected is depend on user’s specification, for
instance, if user need to find 10 outliers in a given data set, the algorithm will run for 10
iterations.
5
6. 4.2 User Interface
The user interface of our application is based on WEKA, in other words, it works WEKA
environment. So its interface looks like WEKA, but the differences are dealing with
spatial outliers effectively. To find outliers, there are 3 kinds of user specified feature:
chosen data file, types of confidence interval, the number of outliers. These features
allow users to figure out different outlier sets that are founded depending on their
choices. And users can find outliers again and again, it means, they can detect different
outlier sets on same data set continuously.
And our system provides detected outliers through 3 different ways: plain text, overall
traffic volume for one day, and neighbor relationship between stations. ‘Outlier result’
panel display plain text, which consist of detail information about time slots of one day,
measured time, stations, and their volume. And users can see overall view of this
information on one image with two graph, one is an average traffic volume at each time
and each station and detected outliers given timeslot and stations. Different colors of the
graphs indicate different volume. It would be helpful to get a big idea about the outliers.
Last visual result is image to show volume of user specified station and its neighborhood.
Using this image, users see 3 different traffic volume graphs and can compare them each
other. This enable for users to analyze relationship between user specified station and its
neighborhood. For example, suppose we want to see traffic volume of station 24. The
system displays traffic volume of station 23, 24, and 25. From this one, users know
pattern of traffic volume of station 23 and 25 are very similar but not station 24 so station
24 should be one of outliers. As mentioned so far, interface of our system consist of
several visual components to use easily rather than command line. User-centered
interface is big difference from existing systems.
5. Methodology
Constructing several experiments to test how exactly find outliers using different spatial
data .
1) Case study
6
7. We will find a set of outliers using different data sets then analyze how exactly they are
found.
6. Contributions
Major contribution of this project is development application to find spatial outlier using
WEKA system. WEKA provides basic data mining functions but these are working on
non-spatial database. Building a new class which can detect sets of spatial outliers using
given spatial data asset and incorporating the class in existing WEKA will enable the
discovery of unexpected, interesting, and useful spatial patterns for further analysis.
7. Conclusion
still working on
8. Future work
- upgrade to allow various file format and data type
- provide written analysis about outlier information
- experiments to find more efficient algorithm using different outlier detection
algorithms.
- Some tool to compare or contrast analysis of different result from different options
to detect outliers
References
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