•

0 likes•356 views

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.

Report

Share

Report

Share

Download to read offline

Concurrent Replication of Parallel and Distributed Simulations

Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation.

50120140505013

This document describes a new distance-based clustering algorithm (DBCA) that aims to improve upon K-means clustering. DBCA selects initial cluster centroids based on the total distance of each data point to all other points, rather than random selection. It calculates distances between all points, identifies points with maximum total distances, and sets initial centroids as the averages of groups of these maximally distant points. The algorithm is compared to K-means, hierarchical clustering, and hierarchical partitioning clustering on synthetic and real data. Experimental results show DBCA produces better quality clusters than these other algorithms.

TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...

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.

Cure, Clustering Algorithm

The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.

MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace Data

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.

Clustering

This document describes a new clustering tool for data mining called RAPID MINER. It discusses the need for clustering in applications like customer segmentation. The project aims to develop a new clustering algorithm using preprocessing techniques like removing null values and redundant data. It will implement clustering to distribute data into groups so that association is strong within clusters and weak between clusters. The document compares the new tool to Weka, discusses how it uses KD trees to improve efficiency over K-means clustering, and concludes that the new algorithm chooses better starting clusters and filters data faster using KD trees.

Big data Clustering Algorithms And Strategies

The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.

Data clustering using kernel based

In recent machine learning community, there is a trend of constructing a linear logarithm version of
nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel
fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering
algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a
nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then
clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides
in increased features specs of dimensions and therefore lack intuitive and clear descriptions without
utilizing added approximation of projection from the specs to the data as executed in the literature
presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the
conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm
(KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange
the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space
of data so that the results of clustering could be interpreted and reformulated in the spaces which are
original. This property is used for clustering incomplete data. Execution on supposed data illustrate that
KFCM has improved performance of clustering and stout as compare to other transformations of FCM for
clustering incomplete data.

Concurrent Replication of Parallel and Distributed Simulations

Parallel and distributed simulations enable the analysis of complex systems by concurrently exploiting the aggregate computation power and memory of clusters of execution units. In this work we investigate a new direction for increasing both the speedup of a simulation process and the utilization of computation and communication resources. Many simulation-based investigations require to collect independent observations for a correct and significant statistical analysis of results. The execution of many independent parallel or distributed simulation runs may suffer the speedup reduction due to rollbacks under the optimistic approach, and due to idle CPU times originated by synchronization and communication bottlenecks under the conservative approach. We present a parallel and distributed simulation framework supporting concurrent replication of parallel and distributed simulations (CR-PADS), as an alternative to the execution of a linear sequence of multiple parallel or distributed simulation runs. Results obtained from tests executed under variable scenarios show that speedup and resource utilization gains could be obtained by adopting the proposed replication approach in addition to the pure parallel and distributed simulation.

50120140505013

This document describes a new distance-based clustering algorithm (DBCA) that aims to improve upon K-means clustering. DBCA selects initial cluster centroids based on the total distance of each data point to all other points, rather than random selection. It calculates distances between all points, identifies points with maximum total distances, and sets initial centroids as the averages of groups of these maximally distant points. The algorithm is compared to K-means, hierarchical clustering, and hierarchical partitioning clustering on synthetic and real data. Experimental results show DBCA produces better quality clusters than these other algorithms.

TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...

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.

Cure, Clustering Algorithm

The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.

MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace Data

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.

Clustering

This document describes a new clustering tool for data mining called RAPID MINER. It discusses the need for clustering in applications like customer segmentation. The project aims to develop a new clustering algorithm using preprocessing techniques like removing null values and redundant data. It will implement clustering to distribute data into groups so that association is strong within clusters and weak between clusters. The document compares the new tool to Weka, discusses how it uses KD trees to improve efficiency over K-means clustering, and concludes that the new algorithm chooses better starting clusters and filters data faster using KD trees.

Big data Clustering Algorithms And Strategies

The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.

Data clustering using kernel based

In recent machine learning community, there is a trend of constructing a linear logarithm version of
nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel
fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering
algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a
nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then
clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides
in increased features specs of dimensions and therefore lack intuitive and clear descriptions without
utilizing added approximation of projection from the specs to the data as executed in the literature
presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the
conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm
(KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange
the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space
of data so that the results of clustering could be interpreted and reformulated in the spaces which are
original. This property is used for clustering incomplete data. Execution on supposed data illustrate that
KFCM has improved performance of clustering and stout as compare to other transformations of FCM for
clustering incomplete data.

DATA MINING:Clustering Types

The document discusses different methods for partitioning data into clusters. It describes hierarchical, density-based, grid-based, and model-based partitioning methods. It then explains the k-means and k-medoids partitioning algorithms in more detail, outlining the basic steps of assigning objects to clusters and updating centroids or medoids. Finally, it summarizes the Birch, ROCK, and CURE clustering algorithms.

3.3 hierarchical methods

Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.

EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...

This paper presents a new method for evaluating the symmetric information gap between two dynamical systems using particle filters. It first describes a symmetric version of the information gap metric based on symmetric Kullback-Leibler divergence. A numerical method is then developed to approximate this symmetric K-L rate using particle filters. This represents the posterior densities of the dynamical systems as mixtures of Gaussians. The method is demonstrated on a nonlinear target tracking example, computing the symmetric information gap between two systems at each time step.

Parallel KNN for Big Data using Adaptive Indexing

This document presents an evaluation of different algorithms for performing parallel k-nearest neighbor (kNN) queries on big data using the MapReduce framework. It first discusses how kNN algorithms do not scale well for large datasets. It then reviews existing MapReduce-based kNN algorithms like H-BNLJ, H-zkNNJ, and RankReduce that improve performance by partitioning data and distributing computation. The document also proposes using an adaptive indexing technique with the RankReduce algorithm. An implementation of this approach on a airline on-time statistics dataset shows it achieves better precision and speed than other algorithms.

Birch

The document summarizes the Birch clustering algorithm. It introduces the key concepts of Birch including clustering features (CF), which summarize information about clusters, and clustering feature trees (CFT), which are hierarchical data structures that store CFs. Birch uses a single scan to incrementally build a CFT, and then performs additional scans to improve clustering quality. It scales well to large databases due to the CF and CFT structures.

Hierarchical clustering

Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. There are two types of hierarchical clustering methods: divisive, which starts with all observations in one cluster and splits recursively; and agglomerative, which starts with each observation in its own cluster and merges pairs of clusters as it moves up the hierarchy. Popular algorithms like BIRCH and CURE were introduced to improve upon hierarchical clustering methods by handling large datasets more efficiently and accounting for clusters with irregular shapes. However, hierarchical clustering still has weaknesses like inability to undo merges and poor scaling to large datasets.

Principal Component Analysis

The aim of this report is to use eigenvectors, eigenvalues, and orthogonality to understand the concept of Principal Component Analysis (PCA) and to show why PCA is useful.

presentation 2019 04_09_rev1

- The document analyzes electricity consumption at home using the K-means clustering algorithm on a dataset and with 1/8 of the original dataset.
- With the full dataset, the optimal number of clusters was found to be 7, with a silhouette score of 0.799. Using 1/8 of the data, the optimal number of clusters was also 7, with a similar silhouette score of 0.810.
- The analysis shows that even with a smaller dataset, the K-means clustering produced similar results to those from the original larger dataset, suggesting the approach could help analyze large datasets more efficiently.

8.clustering algorithm.k means.em algorithm

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.

A046010107

The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.

New Approach for K-mean and K-medoids Algorithm

K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.

SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS

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.

Pillar k means

This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.

IRJET- Different Data Mining Techniques for Weather Prediction

This document discusses different data mining techniques that can be used for weather prediction, including back propagation, decision trees, k-means clustering, expectation maximization, and numerical and statistical methods. It provides an overview of each technique, explaining the basic process or algorithm involved. For example, it explains that back propagation is a deep learning algorithm that trains multilayer neural networks in two phases - propagation and weight updating. It also discusses how decision trees use rules to classify weather data based on input parameters, and how k-means clustering groups similar weather observations into clusters. The document aims to compare these techniques for applying data mining to weather forecasting.

Application of stochastic modelling in bioinformatics

This document discusses stochastic modeling and its applications in bioinformatics. It defines stochastic models and processes, and explains how they differ from deterministic models in accounting for uncertainty. Some examples of stochastic modeling approaches described include stochastic process algebra using tools like π-calculus and Petri nets, Markov models including Markov chains and hidden Markov models, and BioAmbients for modeling biological systems with mobile boundaries. The document argues that stochastic methods are better suited than deterministic ones for describing complex and dynamically evolving biological systems.

Canopy clustering algorithm

Canopy clustering is an unsupervised pre-clustering algorithm used to speed up K-means and hierarchical clustering on large datasets. It works by first selecting random points as canopy centers and assigning other points within a threshold distance to canopies. It then removes points within a smaller threshold to prevent them from being new centers, repeating until no points remain. This helps reduce the dataset size before the main clustering algorithm is applied.

3.6 constraint based cluster analysis

Constraint-based clustering finds clusters that satisfy user-specified constraints, such as the expected number of clusters or minimum/maximum cluster size. It considers obstacles like rivers or roads that require redefining distance functions. Clustering algorithms are adapted to handle obstacles by using visibility graphs and triangulating regions to reduce distance computation costs. Semi-supervised clustering uses some labeled data to initialize and modify algorithms like k-means to satisfy pairwise constraints.

Clustering using kernel entropy principal component analysis and variable ker...

Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the kernel function into the EPCA, the input space can be mapped implicitly into a high-dimensional of feature space. Then, the Shannon’s entropy estimated via the inertia provided by the contribution of every mapped object in data is the key measure to determine the optimal extracted features space. Our proposed method performs very well the clustering algorithm of the fast search of clusters’ centers based on the local densities’ computing. Experimental results disclose that the approach is feasible and efficient on the performance query.

Clustering

Clustering algorithms are a type of unsupervised learning that groups unlabeled data points together based on similarities. There are many different clustering methods that can handle numeric and/or symbolic data in either hierarchical or flat clustering structures. One simple and commonly used algorithm is k-means clustering, which assigns data points to k clusters based on minimizing distances between points and assigned cluster centers. K-means clustering has advantages of being simple and automatically assigning data points but has disadvantages such as needing to pre-specify the number of clusters and being sensitive to outliers.

Data clustering using map reduce

This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering

Not Only Statements: The Role of Textual Analysis in Software Quality

My keynote at the 2012 Workshop on Mining Unstructured Data (co-located with the 10th Working Conference on Reverse Engineering - WCRE'12). Kingston, Ontario, Canada. October 17th, 2012.

Consulting whitepaper enterprise-architecture-transformation-pharmaceutical-c...

The document describes the enterprise architecture definition approach for a large pharmaceutical company undergoing transformation. The approach included assessing the "as-is" state, defining principles and policies, and creating "to-be" architecture views for business, information, applications, and integration. Key deliverables were TO-BE processes, information models, application catalogues, services catalogue, and transition views to guide implementation projects and address challenges such as scope management and stakeholder alignment. The tailored enterprise architecture definition helped bridge the strategy to implementation of the company's transformation.

DATA MINING:Clustering Types

The document discusses different methods for partitioning data into clusters. It describes hierarchical, density-based, grid-based, and model-based partitioning methods. It then explains the k-means and k-medoids partitioning algorithms in more detail, outlining the basic steps of assigning objects to clusters and updating centroids or medoids. Finally, it summarizes the Birch, ROCK, and CURE clustering algorithms.

3.3 hierarchical methods

Hierarchical clustering methods group data points into a hierarchy of clusters based on their distance or similarity. There are two main approaches: agglomerative, which starts with each point as a separate cluster and merges them; and divisive, which starts with all points in one cluster and splits them. AGNES and DIANA are common agglomerative and divisive algorithms. Hierarchical clustering represents the hierarchy as a dendrogram tree structure and allows exploring data at different granularities of clusters.

EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...

This paper presents a new method for evaluating the symmetric information gap between two dynamical systems using particle filters. It first describes a symmetric version of the information gap metric based on symmetric Kullback-Leibler divergence. A numerical method is then developed to approximate this symmetric K-L rate using particle filters. This represents the posterior densities of the dynamical systems as mixtures of Gaussians. The method is demonstrated on a nonlinear target tracking example, computing the symmetric information gap between two systems at each time step.

Parallel KNN for Big Data using Adaptive Indexing

This document presents an evaluation of different algorithms for performing parallel k-nearest neighbor (kNN) queries on big data using the MapReduce framework. It first discusses how kNN algorithms do not scale well for large datasets. It then reviews existing MapReduce-based kNN algorithms like H-BNLJ, H-zkNNJ, and RankReduce that improve performance by partitioning data and distributing computation. The document also proposes using an adaptive indexing technique with the RankReduce algorithm. An implementation of this approach on a airline on-time statistics dataset shows it achieves better precision and speed than other algorithms.

Birch

The document summarizes the Birch clustering algorithm. It introduces the key concepts of Birch including clustering features (CF), which summarize information about clusters, and clustering feature trees (CFT), which are hierarchical data structures that store CFs. Birch uses a single scan to incrementally build a CFT, and then performs additional scans to improve clustering quality. It scales well to large databases due to the CF and CFT structures.

Hierarchical clustering

Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. There are two types of hierarchical clustering methods: divisive, which starts with all observations in one cluster and splits recursively; and agglomerative, which starts with each observation in its own cluster and merges pairs of clusters as it moves up the hierarchy. Popular algorithms like BIRCH and CURE were introduced to improve upon hierarchical clustering methods by handling large datasets more efficiently and accounting for clusters with irregular shapes. However, hierarchical clustering still has weaknesses like inability to undo merges and poor scaling to large datasets.

Principal Component Analysis

The aim of this report is to use eigenvectors, eigenvalues, and orthogonality to understand the concept of Principal Component Analysis (PCA) and to show why PCA is useful.

presentation 2019 04_09_rev1

- The document analyzes electricity consumption at home using the K-means clustering algorithm on a dataset and with 1/8 of the original dataset.
- With the full dataset, the optimal number of clusters was found to be 7, with a silhouette score of 0.799. Using 1/8 of the data, the optimal number of clusters was also 7, with a similar silhouette score of 0.810.
- The analysis shows that even with a smaller dataset, the K-means clustering produced similar results to those from the original larger dataset, suggesting the approach could help analyze large datasets more efficiently.

8.clustering algorithm.k means.em algorithm

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.

A046010107

The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.

New Approach for K-mean and K-medoids Algorithm

K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.

SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS

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.

Pillar k means

This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.

IRJET- Different Data Mining Techniques for Weather Prediction

This document discusses different data mining techniques that can be used for weather prediction, including back propagation, decision trees, k-means clustering, expectation maximization, and numerical and statistical methods. It provides an overview of each technique, explaining the basic process or algorithm involved. For example, it explains that back propagation is a deep learning algorithm that trains multilayer neural networks in two phases - propagation and weight updating. It also discusses how decision trees use rules to classify weather data based on input parameters, and how k-means clustering groups similar weather observations into clusters. The document aims to compare these techniques for applying data mining to weather forecasting.

Application of stochastic modelling in bioinformatics

This document discusses stochastic modeling and its applications in bioinformatics. It defines stochastic models and processes, and explains how they differ from deterministic models in accounting for uncertainty. Some examples of stochastic modeling approaches described include stochastic process algebra using tools like π-calculus and Petri nets, Markov models including Markov chains and hidden Markov models, and BioAmbients for modeling biological systems with mobile boundaries. The document argues that stochastic methods are better suited than deterministic ones for describing complex and dynamically evolving biological systems.

Canopy clustering algorithm

Canopy clustering is an unsupervised pre-clustering algorithm used to speed up K-means and hierarchical clustering on large datasets. It works by first selecting random points as canopy centers and assigning other points within a threshold distance to canopies. It then removes points within a smaller threshold to prevent them from being new centers, repeating until no points remain. This helps reduce the dataset size before the main clustering algorithm is applied.

3.6 constraint based cluster analysis

Constraint-based clustering finds clusters that satisfy user-specified constraints, such as the expected number of clusters or minimum/maximum cluster size. It considers obstacles like rivers or roads that require redefining distance functions. Clustering algorithms are adapted to handle obstacles by using visibility graphs and triangulating regions to reduce distance computation costs. Semi-supervised clustering uses some labeled data to initialize and modify algorithms like k-means to satisfy pairwise constraints.

Clustering using kernel entropy principal component analysis and variable ker...

Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the kernel function into the EPCA, the input space can be mapped implicitly into a high-dimensional of feature space. Then, the Shannon’s entropy estimated via the inertia provided by the contribution of every mapped object in data is the key measure to determine the optimal extracted features space. Our proposed method performs very well the clustering algorithm of the fast search of clusters’ centers based on the local densities’ computing. Experimental results disclose that the approach is feasible and efficient on the performance query.

Clustering

Clustering algorithms are a type of unsupervised learning that groups unlabeled data points together based on similarities. There are many different clustering methods that can handle numeric and/or symbolic data in either hierarchical or flat clustering structures. One simple and commonly used algorithm is k-means clustering, which assigns data points to k clusters based on minimizing distances between points and assigned cluster centers. K-means clustering has advantages of being simple and automatically assigning data points but has disadvantages such as needing to pre-specify the number of clusters and being sensitive to outliers.

Data clustering using map reduce

This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering

DATA MINING:Clustering Types

DATA MINING:Clustering Types

3.3 hierarchical methods

3.3 hierarchical methods

EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...

EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE...

Parallel KNN for Big Data using Adaptive Indexing

Parallel KNN for Big Data using Adaptive Indexing

Birch

Birch

Hierarchical clustering

Hierarchical clustering

Principal Component Analysis

Principal Component Analysis

presentation 2019 04_09_rev1

presentation 2019 04_09_rev1

8.clustering algorithm.k means.em algorithm

8.clustering algorithm.k means.em algorithm

A046010107

A046010107

New Approach for K-mean and K-medoids Algorithm

New Approach for K-mean and K-medoids Algorithm

SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS

SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS

Pillar k means

Pillar k means

IRJET- Different Data Mining Techniques for Weather Prediction

IRJET- Different Data Mining Techniques for Weather Prediction

Application of stochastic modelling in bioinformatics

Application of stochastic modelling in bioinformatics

Canopy clustering algorithm

Canopy clustering algorithm

3.6 constraint based cluster analysis

3.6 constraint based cluster analysis

Clustering using kernel entropy principal component analysis and variable ker...

Clustering using kernel entropy principal component analysis and variable ker...

Clustering

Clustering

Data clustering using map reduce

Data clustering using map reduce

Not Only Statements: The Role of Textual Analysis in Software Quality

My keynote at the 2012 Workshop on Mining Unstructured Data (co-located with the 10th Working Conference on Reverse Engineering - WCRE'12). Kingston, Ontario, Canada. October 17th, 2012.

Consulting whitepaper enterprise-architecture-transformation-pharmaceutical-c...

The document describes the enterprise architecture definition approach for a large pharmaceutical company undergoing transformation. The approach included assessing the "as-is" state, defining principles and policies, and creating "to-be" architecture views for business, information, applications, and integration. Key deliverables were TO-BE processes, information models, application catalogues, services catalogue, and transition views to guide implementation projects and address challenges such as scope management and stakeholder alignment. The tailored enterprise architecture definition helped bridge the strategy to implementation of the company's transformation.

A2DataDive workshop: Introduction to R

A2DataDive workshop speakers Alex Ocampo, Chong Zhang, Sangwon Hyun, Yiqun Hu. A2 Data Dive. Feb. 10- 12, 2012. visit the wiki for more information: http://wiki.datawithoutborders.cc/index.php?title=Project:Current_events:A2_DD

Preliminary Study of Engineering Self

This document describes a study that aimed to develop a measure of engineering self-efficacy (ESE) based on an analysis of survey questions administered to first-year engineering students. The study identified 10 questions across 5 domains that measured a single ESE construct. Students in a course only open to engineering majors reported higher ESE than students in a course open to both engineering and non-engineering majors. This preliminary analysis provides initial evidence that the identified questions capture differences in ESE between these student groups. Further validation of the ESE measure is needed.

Kent ro systems

Aquatech Kent RO water purifier annual maintenance service offers 1 year maintenance service to domestic and home used RO systems water purifier with spare parts replacement assistance.

Selected ion flow tube MS - Online quantitative VOC analysis

SIFT-MS accurately identifies and quantifies volatile compounds. The analysis occurs through a process of chemical ionization in a flow tube.
To analyze volatile compounds, a sample is introduced into the flow tube at a precisely controlled rate. Inside the flow tube reagent ions react with volatile compounds present in the sample. This reaction forms product ions, which are analyzed by a quadrupole mass spectrometer and particle multiplier. The result is spectra, which instantly identify and quantify volatile compounds.
Analysis is performed by a Voice Series instrument, which can be based in a laboratory, on a production line, or in a vehicle. Results can be automatically exported to other systems, such as production line controllers.
We provide turnkey solutions, or you can create your own analysis suites and protocols, including how results are processed and presented.

Not Only Statements: The Role of Textual Analysis in Software Quality

Not Only Statements: The Role of Textual Analysis in Software Quality

Consulting whitepaper enterprise-architecture-transformation-pharmaceutical-c...

Consulting whitepaper enterprise-architecture-transformation-pharmaceutical-c...

A2DataDive workshop: Introduction to R

A2DataDive workshop: Introduction to R

Preliminary Study of Engineering Self

Preliminary Study of Engineering Self

Kent ro systems

Kent ro systems

Selected ion flow tube MS - Online quantitative VOC analysis

Selected ion flow tube MS - Online quantitative VOC analysis

An Efficient Clustering Method for Aggregation on Data Fragments

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.

A PSO-Based Subtractive Data Clustering Algorithm

There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.

Mine Blood Donors Information through Improved K-Means Clustering

The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessity for the organized analysis of the blood donor database or blood banks repositories. Clustering analysis is one of the data mining applications and K-means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original k-means algorithm by improving the initial centroids with distribution of data. Results and discussions show that improved K-means algorithm produces accurate clusters in less computation time to find the donors information

Experimental study of Data clustering using k- Means and modified algorithms

The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time

Comparison Between Clustering Algorithms for Microarray Data Analysis

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.

Unsupervised Learning.pptx

Model Selection and Evaluation
Dimensionality Reduction
Artificial intelligence
Machine Learning
Supervised Learning
Unsupervised Learning

An improvement in k mean clustering algorithm using better time and accuracy

This document summarizes a research paper that proposes an improved K-means clustering algorithm to enhance accuracy and reduce computation time. The standard K-means algorithm randomly selects initial cluster centroids, affecting results. The proposed algorithm systematically determines initial centroids based on data point distances. It assigns data to the closest initial centroid to generate initial clusters. Iteratively, it calculates new centroids and reassigns data only if distances decrease, reducing unnecessary computations. Experiments on various datasets show the proposed algorithm achieves higher accuracy faster than standard K-means.

Data Mining: Cluster Analysis

Clustering is the process of making a group of abstract objects into classes of similar objects. Clustering helps to splits data into several subsets. Each of these subsets contains data similar to each other, and these subsets are called clusters. Now that the data from our customer base is divided into clusters, we can make an informed decision about who we think is best suited for this product.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.

Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...

This document analyzes a dataset of diabetes records from 130 US hospitals from 1999-2008 using various statistical data analysis and machine learning techniques. It first performs dimensionality reduction using principal component analysis (PCA) and multidimensional scaling (MDS). It then clusters the data using hierarchical clustering and k-means clustering. Cluster validity is assessed using precision. Spectral clustering is also applied and validated using Dunn and Davies-Bouldin indexes, with complete linkage diameter performing best.

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.

CLUSTERING IN DATA MINING.pdf

Clustering is an unsupervised machine learning technique used to group unlabeled data points. There are two main approaches: hierarchical clustering and partitioning clustering. Partitioning clustering algorithms like k-means and k-medoids attempt to partition data into k clusters by optimizing a criterion function. Hierarchical clustering creates nested clusters by merging or splitting clusters. Examples of hierarchical algorithms include agglomerative clustering, which builds clusters from bottom-up, and divisive clustering, which separates clusters from top-down. Clustering can group both numerical and categorical data.

An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...

An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...Happiest Minds Technologies

This document presents an approach for clustering a mixed dataset containing both numeric and categorical attributes using an ART-2 neural network model. The dataset contains daily stock price data with 19 attributes describing comparisons between consecutive days. Clustering mixed datasets is challenging due to different attribute types. The ART-2 model is used to classify the dataset without requiring a distance function. Then an autoencoder model reduces the dimensionality to allow visual validation of the clusters. The results demonstrate the ART-2 model's ability to cluster complex, mixed datasets.CONVOLUTIONAL NEURAL NETWORK BASED RETINAL VESSEL SEGMENTATION

In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel
from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of
blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and
cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels.
In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to
extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked
Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.

Convolutional Neural Network based Retinal Vessel Segmentation

This document discusses a proposed method for retinal vessel segmentation using convolutional neural networks and stacked autoencoders. The method extracts patches from fundus images, performs preprocessing including normalization and whitening, then trains a CNN and stacked autoencoder on the patches. Based on experiments, the stacked autoencoder and CNN achieved 90% and 95% accuracy, respectively, for vessel segmentation. Evaluation metrics like accuracy, sensitivity and specificity are used to assess the method's performance on test datasets.

IRJET- Customer Segmentation from Massive Customer Transaction Data

This document discusses various methods for customer segmentation through analysis of massive customer transaction data, including K-Means clustering, PAM clustering, agglomerative clustering, divisive clustering, and density-based clustering. It finds that K-Means is the most commonly used partitioning method. The document also reviews related work on customer segmentation and clustering algorithms like CLARA, CLARANS, BIRCH, ROCK, CHAMELEON, CURE, DHCC, DBSCAN, and LOF. It proposes a framework for an online shopping site that would apply these techniques to group customers based on their product preferences in transaction data.

Performance Analysis of Different Clustering Algorithm

This document discusses and compares different clustering algorithms for outlier detection: PAM, CLARA, CLARANS, and ECLARANS. It provides an overview of how each algorithm works, including describing the procedures and steps involved. The proposed work is to modify the ECLARANS algorithm to improve its accuracy and time efficiency for outlier detection by selecting cluster nodes based on maximum distance between data points rather than randomly. This is expected to reduce the number of iterations needed.

F017132529

This document discusses and compares different clustering algorithms for outlier detection: PAM, CLARA, CLARANS, and ECLARANS. It proposes a modified ECLARANS algorithm that selects nodes with maximum distance between data points rather than random selection to improve accuracy and efficiency of outlier detection. The algorithms are implemented on a dataset and execution times are recorded. Results show the modified ECLARANS has better time performance than other algorithms for outlier detection.

Az36311316

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.

Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...

In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm

An Efficient Clustering Method for Aggregation on Data Fragments

An Efficient Clustering Method for Aggregation on Data Fragments

A PSO-Based Subtractive Data Clustering Algorithm

A PSO-Based Subtractive Data Clustering Algorithm

Mine Blood Donors Information through Improved K-Means Clustering

Mine Blood Donors Information through Improved K-Means Clustering

Experimental study of Data clustering using k- Means and modified algorithms

Experimental study of Data clustering using k- Means and modified algorithms

Comparison Between Clustering Algorithms for Microarray Data Analysis

Comparison Between Clustering Algorithms for Microarray Data Analysis

Unsupervised Learning.pptx

Unsupervised Learning.pptx

An improvement in k mean clustering algorithm using better time and accuracy

An improvement in k mean clustering algorithm using better time and accuracy

Data Mining: Cluster Analysis

Data Mining: Cluster Analysis

Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...

Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...

CLUSTERING IN DATA MINING.pdf

CLUSTERING IN DATA MINING.pdf

An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...

An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...

CONVOLUTIONAL NEURAL NETWORK BASED RETINAL VESSEL SEGMENTATION

CONVOLUTIONAL NEURAL NETWORK BASED RETINAL VESSEL SEGMENTATION

Convolutional Neural Network based Retinal Vessel Segmentation

Convolutional Neural Network based Retinal Vessel Segmentation

IRJET- Customer Segmentation from Massive Customer Transaction Data

IRJET- Customer Segmentation from Massive Customer Transaction Data

Performance Analysis of Different Clustering Algorithm

Performance Analysis of Different Clustering Algorithm

F017132529

F017132529

Az36311316

Az36311316

Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...

Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...

12th CONTECSI USP - Guia para publicar Andre Jun Emerald

Este documento fornece um guia sobre como publicar artigos acadêmicos. Ele discute o processo de publicação, incluindo escolher uma revista apropriada, estruturar o artigo, escrever um resumo e lidar com a revisão. Também aborda questões éticas como plágio e direitos autorais. O objetivo é ajudar os autores a produzir trabalhos de alta qualidade e ter sucesso na publicação.

12 contecsi IT Management GAESI USP Rastreabilidade de Medicamentos - Elcio...

O documento discute os problemas da falsificação e contrabando de medicamentos no Brasil, como a evasão fiscal de bilhões e centenas de milhares de mortes por ano. A rastreabilidade de medicamentos é proposta como a principal solução, exigindo que todos os medicamentos tenham um número de identificação e que cada elo da cadeia de suprimentos reporte os movimentos. A lei brasileira estabelece prazos para a implantação até 2016.

12 contecsi Workshop Mendeley Ligia Capobianco

Mendeley is a free reference manager and academic social network that can be used to organize research papers and citations, collaborate with other researchers, and discover new papers. It allows users to organize PDF documents and references, sync their libraries across devices, manually add references or import them from other databases, annotate and highlight PDFs, and generate citations and bibliographies in Microsoft Word. Users can also create and join public and private groups to share papers. Mendeley provides recommendations of new research papers based on a user's library and can help users stay up-to-date on the latest papers in their fields.

Planejamento e gestão de indicadores para projetos digitais - Workshop 12th C...

Planejamento e gestão de indicadores para projetos digitais
Objetivos de um projeto digital
•Técnicas de operação de dados
•Metodologia 5W2H
•Caso de uso

Programa de Apoio às Publicações Científicas Periódicas da USP - 12th CONTECSI

O documento descreve o Programa de Apoio às Publicações Científicas Periódicas da USP, que tem como objetivo promover políticas e ações para melhorar a qualidade e internacionalização das revistas científicas da USP. O programa oferece serviços como hospedagem online, atribuição de identificadores digitais, preservação digital e apoio financeiro. O Portal de Revistas da USP reúne as publicações da universidade e teve aumento significativo no número de artigos e acessos.

Centro Integrado de Mobilidade Urbana CIMU - 12th CONTECSI

O documento discute a proposta de implementação de um Centro Integrado de Mobilidade Urbana (CIMU) em São Paulo, que centralizaria as informações de trânsito e transporte para melhor coordenar a mobilidade na cidade. O CIMU integraria todos os sistemas existentes e permitiria visualizar dados de forma global para tomada de decisões, automatizar processos, implementar novas funcionalidades e compartilhar recursos. Isso reduziria custos e melhoraria a oferta, coordenação e satisfação dos usuários.

The ladder of Citizens ‘smartness’ Citizens participation in smart cities - 1...

Citizens can engage in trade-offs with powerholders (Partnership), obtain the majority of decision-making seats (Delegated Power), or full managerial power (Citizen Control).the havenots are allowed to hear (In-forming), to have a voice (Consultation) and to ad-vise, but the powerholders retain the right to decide (Placation)

Papel de los vocabularios semánticos en la economía en red - 12th CONTECSI

O documento discute o papel dos vocabulários semânticos na economia da internet. Apresenta diferentes tipos de vocabulários como folksonomias, taxonomias e ontologias e discute suas vantagens e desvantagens. Também aborda como empresas e instituições usam sites como o Flickr para compartilhar e organizar fotografias usando folksonomias.

Balance Innovations in Backoffice Improvement and Service Delivery A study ca...

Balance Innovations in Backoffice Improvement and Service Delivery
A study case of Civil Registration System in Angola.

Sistema Autenticador e Transmissor (SAT): modelo tecnológico de automação e c...

O documento propõe o Sistema Autenticador e Transmissor (SAT), um modelo tecnológico para automatizar processos em cidades inteligentes, aplicado inicialmente ao setor tributário. O SAT centralizaria dados de vendas no varejo para análise fiscal e combate à sonegação, simplificando obrigações para contribuintes e melhorando o controle do fisco.

GAESI - Gestão em Automação e TI - 12th CONTECSI

O documento discute a implementação de um sistema integrado chamado Porto Azul para melhorar a eficiência da cadeia logística portuária. O sistema permitiria a gestão e rastreabilidade de cargas desde o início até o fim do processo, reduzindo custos, antecipando problemas e melhorando a produtividade. Próximos passos incluem mapear processos de negócios, especificar requisitos, identificar usuários e realizar testes piloto antes do lançamento completo.

Co-production: an opportunity toward better digital governance - 12th CONTECSI

O documento discute a governança digital no setor público brasileiro. Ele descreve governança digital como a utilização de tecnologias da informação pelo governo para melhorar o acesso à informação, prestação de serviços e participação social. O documento também discute estratégias do governo brasileiro para aprimorar a governança digital, incluindo a simplificação de serviços, integração de sistemas e abertura de dados governamentais.

The Digital Transformation - Challenges and Opportunities for IS researchers ...

Technische Universität München, Prof. Dr. H. Krcmar: The Digital Transformation -
Challenges and Opportunities for IS researchers.

Auditoria Contínua e o Sistema de Controle da Administração Pública Federal -...

O documento descreve os sistemas de controle da administração pública federal no Brasil, incluindo o sistema de controle interno e externo. Ele também discute a importância da auditoria contínua nesses órgãos para monitorar riscos e controles de forma permanente.

Big (huge) Data and a continuous and predictive audit: new evidence, new met...

Big (huge) Data and a continuous and predictive audit: new evidence, new methods, a reconceptualization.

Text Mining and Continuous Assurance Kevin Moffitt - 12th CONTECSI 34th WCARS

Continuous Assurance
• Allows for the automated and frequent review of business data
• Current focus is on the structured data
– General ledgers
– Financial statements
– XBRL
• However, we cannot ignore the information found in unstructured data
–Textual data, for example narrative portion of financial disclosures
• Up to 85% of the data in financial disclosures is in the form of text

Federal Audit in Relation to Continuous Audit - 12th CONTECSI 34th WCARS

This document discusses using continuous auditing and XBRL tagging of government financial reports to improve access and analysis of those reports. It notes that current government financial reports like CAFRs are often only available as PDFs from various websites, making them difficult for automated analysis. The document advocates tagging CAFRs and other reports with XBRL to make their contents machine-readable, allowing more time to be spent on analysis rather than data extraction. With XBRL tagging, reports could be stored in a central repository and continuous auditing could generate real-time, standardized reports for various stakeholders like citizens, legislators, and analysts.

O Tribunal de Contas da União e a Auditoria Contínua - 12th CONTECSI 34th WCARS

O documento discute o plano de controle externo do Tribunal de Contas da União para 2015-2017, que inclui auditorias contínuas e preditivas para melhorar a fiscalização e a transparência da administração pública em benefício dos cidadãos, utilizando tecnologias como dados abertos.

Cenário atual da Auditoria Contínua em Bancos no Brasil Itaú-Unibanco Holding...

O documento descreve a auditoria contínua em bancos brasileiros, com foco no Itaú Unibanco. Ele discute as tendências de auditoria, como análise de dados, big data e inteligência artificial. Também apresenta a estrutura de auditoria contínua do Itaú, que utiliza equipes centralizadas para automação de testes e indicadores, e ferramentas como ACL, SAS e Tableau.

Auditoria Eletrônica: Automatização de procedimentos de auditoria através do ...

Auditoria Eletrônica: Automatização de procedimentos de auditoria através do uso de ferramentas de análise de dados. Provê relevante ganho de performance na execução e abrangência de análise. Nos referimos aos testes automatizados como CAATs.
•Auditoria Contínua: Avaliação de risco de forma perene ao longo do tempo através do uso de indicadores ou de técnicas de monitoramento como participação em fóruns, leitura de relatórios, reuniões periódicas, acompanhamento do mercado, dentre outras atividades.
•Análise de Dados / Datamining: Uso de técnicas estatísticas para identificação de comportamentos ou tendências atreladas ao risco da área ou processo.
•Big Data: Acesso a grande volume de dados, tanto de origem endógena como exógena, com processamento rápido em função de sua característica de replicação de dados. Aliado ao uso de técnicas estatísticas e cruzamento de informações permite a identificação de comportamentos, padrões, tendências, etc.

12th CONTECSI USP - Guia para publicar Andre Jun Emerald

12th CONTECSI USP - Guia para publicar Andre Jun Emerald

12 contecsi IT Management GAESI USP Rastreabilidade de Medicamentos - Elcio...

12 contecsi IT Management GAESI USP Rastreabilidade de Medicamentos - Elcio...

12 contecsi Workshop Mendeley Ligia Capobianco

12 contecsi Workshop Mendeley Ligia Capobianco

Planejamento e gestão de indicadores para projetos digitais - Workshop 12th C...

Planejamento e gestão de indicadores para projetos digitais - Workshop 12th C...

Programa de Apoio às Publicações Científicas Periódicas da USP - 12th CONTECSI

Programa de Apoio às Publicações Científicas Periódicas da USP - 12th CONTECSI

Centro Integrado de Mobilidade Urbana CIMU - 12th CONTECSI

Centro Integrado de Mobilidade Urbana CIMU - 12th CONTECSI

The ladder of Citizens ‘smartness’ Citizens participation in smart cities - 1...

The ladder of Citizens ‘smartness’ Citizens participation in smart cities - 1...

Papel de los vocabularios semánticos en la economía en red - 12th CONTECSI

Papel de los vocabularios semánticos en la economía en red - 12th CONTECSI

Balance Innovations in Backoffice Improvement and Service Delivery A study ca...

Balance Innovations in Backoffice Improvement and Service Delivery A study ca...

Sistema Autenticador e Transmissor (SAT): modelo tecnológico de automação e c...

Sistema Autenticador e Transmissor (SAT): modelo tecnológico de automação e c...

GAESI - Gestão em Automação e TI - 12th CONTECSI

GAESI - Gestão em Automação e TI - 12th CONTECSI

Co-production: an opportunity toward better digital governance - 12th CONTECSI

Co-production: an opportunity toward better digital governance - 12th CONTECSI

The Digital Transformation - Challenges and Opportunities for IS researchers ...

The Digital Transformation - Challenges and Opportunities for IS researchers ...

Auditoria Contínua e o Sistema de Controle da Administração Pública Federal -...

Auditoria Contínua e o Sistema de Controle da Administração Pública Federal -...

Big (huge) Data and a continuous and predictive audit: new evidence, new met...

Big (huge) Data and a continuous and predictive audit: new evidence, new met...

Text Mining and Continuous Assurance Kevin Moffitt - 12th CONTECSI 34th WCARS

Text Mining and Continuous Assurance Kevin Moffitt - 12th CONTECSI 34th WCARS

Federal Audit in Relation to Continuous Audit - 12th CONTECSI 34th WCARS

Federal Audit in Relation to Continuous Audit - 12th CONTECSI 34th WCARS

O Tribunal de Contas da União e a Auditoria Contínua - 12th CONTECSI 34th WCARS

O Tribunal de Contas da União e a Auditoria Contínua - 12th CONTECSI 34th WCARS

Cenário atual da Auditoria Contínua em Bancos no Brasil Itaú-Unibanco Holding...

Cenário atual da Auditoria Contínua em Bancos no Brasil Itaú-Unibanco Holding...

Auditoria Eletrônica: Automatização de procedimentos de auditoria através do ...

Auditoria Eletrônica: Automatização de procedimentos de auditoria através do ...

Generating privacy-protected synthetic data using Secludy and Milvus

During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.

Serial Arm Control in Real Time Presentation

Serial Arm Control in Real Time

Introduction of Cybersecurity with OSS at Code Europe 2024

I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257

JavaLand 2024: Application Development Green Masterplan

My presentation slides I used at JavaLand 2024

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20

Leveraging the Graph for Clinical Trials and Standards

Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j

What is an RPA CoE? Session 1 – CoE Vision

In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems

Y-Combinator seed pitch deck template PP

Pitch Deck Template

Dandelion Hashtable: beyond billion requests per second on a commodity server

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

Building Production Ready Search Pipelines with Spark and Milvus

Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.

GraphRAG for Life Science to increase LLM accuracy

GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers

TrustArc Webinar - 2024 Global Privacy Survey

How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program

Taking AI to the Next Level in Manufacturing.pdf

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.

"Choosing proper type of scaling", Olena Syrota

Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.

The Microsoft 365 Migration Tutorial For Beginner.pptx

This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/

Programming Foundation Models with DSPy - Meetup Slides

Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.

Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll 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, we’ll 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 sources—from PDF floorplans to web pages—using 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 it’s 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.
We’ll 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!

9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...

9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...

Generating privacy-protected synthetic data using Secludy and Milvus

Generating privacy-protected synthetic data using Secludy and Milvus

Serial Arm Control in Real Time Presentation

Serial Arm Control in Real Time Presentation

Introduction of Cybersecurity with OSS at Code Europe 2024

Introduction of Cybersecurity with OSS at Code Europe 2024

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

JavaLand 2024: Application Development Green Masterplan

JavaLand 2024: Application Development Green Masterplan

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays - June 2024

Leveraging the Graph for Clinical Trials and Standards

Leveraging the Graph for Clinical Trials and Standards

What is an RPA CoE? Session 1 – CoE Vision

What is an RPA CoE? Session 1 – CoE Vision

Y-Combinator seed pitch deck template PP

Y-Combinator seed pitch deck template PP

Dandelion Hashtable: beyond billion requests per second on a commodity server

Dandelion Hashtable: beyond billion requests per second on a commodity server

Building Production Ready Search Pipelines with Spark and Milvus

Building Production Ready Search Pipelines with Spark and Milvus

GraphRAG for Life Science to increase LLM accuracy

GraphRAG for Life Science to increase LLM accuracy

TrustArc Webinar - 2024 Global Privacy Survey

TrustArc Webinar - 2024 Global Privacy Survey

Taking AI to the Next Level in Manufacturing.pdf

Taking AI to the Next Level in Manufacturing.pdf

"Choosing proper type of scaling", Olena Syrota

"Choosing proper type of scaling", Olena Syrota

The Microsoft 365 Migration Tutorial For Beginner.pptx

The Microsoft 365 Migration Tutorial For Beginner.pptx

Programming Foundation Models with DSPy - Meetup Slides

Programming Foundation Models with DSPy - Meetup Slides

Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Driving Business Innovation: Latest Generative AI Advancements & Success Story

- 1. Automated Clustering Project MiklosVasarhelyi, Paul Byrnes, andYunsenWang Presented by DenizAppelbaum
- 2. Motivation Motivation entails the development of a program that automatically performs clustering and outlier detection for a wide variety of numerically represented data.
- 3. Outline of program features Normalizes all data to be clustered Creates normalized principal components from the normalized data Automatically selects the necessary normalized principal components for use in actual clustering and outlier detection Compares a variety of algorithms based upon the selected set of normalized principal components Adopts the top performing model based upon silhouette coefficient values to perform the final clustering and outlier detection procedures Produces relevant information and outputs throughout the process
- 4. Data normalization Data normalization Converts each numerically represented dimension to be clustered into the range [0,1]. A desirable procedure for preparing numeric attributes for clustering
- 5. Principal component analysis Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. In this way, PCA can both reduce dimensionality as well as eliminate inherent problems associated with clustering data whose attributes are correlated In the following slides, a random sample of 5,000 credit card customers is used to demonstrate the automated clustering and outlier detection program
- 6. Principal component analysis PCA initially results in four principal components being generated from the original data Using a cumulative data variability threshold of 80% (default specification), three principal components are automatically selected for analysis – they explain the vast majority of data variability
- 7. Principal component analysis Scatter plot of PC1 and PC2 In this view, the top 2 principal components are plotted for each object in two-dimensional space. As can be seen, a small subset of records appear significantly more distant/different from the vast majority of objects.
- 8. Clustering exploration/simulation process - examples Ward method Ward suggested a general agglomerative hierarchical clustering procedure, where the criterion for choosing the pair of clusters to merge at each step is based on the optimal value of an objective function. Complete link method This method is also known as farthest neighbor clustering.The result of the clustering can be visualized as a dendrogram, which shows the sequence of cluster fusion and the distance at which each fusion took place. PAM (partitioning around medoids) The k-medoids algorithm is a clustering method related to the k-means algorithm and the medoids shift algorithm; It is considered more stable than k-means, because it uses the median rather than mean K-means k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.
- 9. Clustering exploration results The result shown below is based upon a simulation exercise, whereby all four algorithms are automatically compared on the data set (i.e., a random sample of 5,000 records from the credit card customer data). In this particular case, the best model is found to be a two-cluster solution using the complete link hierarchical method. This is the final model and is used for subsequent clustering and outlier detection. Best clustering result: The silhouette value can theoretically range from -1 to +1, with higher values indicative of better cluster quality in terms of both cohesion and separation. Best Method Number Of Clusters SilhouetteValue complete link hierarchical 2 0.753754205720575
- 10. Complete-link Hierarchical clustering (1/2) The 5,000 instances are on the x-axis. In moving vertically from the x-axis, one can begin to see how the actual clusters are formed.
- 11. Plot of PCs with cluster assignment labels (1/3) In this view, the top two principal components (i.e., PC1 and PC2) are plotted for each object in two- dimensional space. In the graph, there are two clusters, one dark blue and the other light blue. The small subset of three records appears substantially more different from the majority of objects.
- 12. Plot of PCs with cluster assignment labels (2/3) In this view, PC1 and PC3 are plotted for each object in two-dimensional space. In the graph, the two clusters are again shown. It is once again evident that the small subset of three records appears more different from the majority of other objects.
- 13. Plot of PCs with cluster assignment labels (3/3) In this view, PC2 and PC3 are plotted for each object in two- dimensional space. Cluster differences appear less prominent from this perspective.
- 14. Principal components 3D scatterplot Cluster one represents the majority class (black) while cluster two represents the rare class (red). In this view, one can clearly see the subset of three records (in red) appearing more isolated from the other objects.
- 15. Cluster 1 outlier plot In this view, an arbitrary cutoff is inserted at the 99.9th percentile (red horizontal line) so as to provide for efficient identification of very irregular records. Objects further from the x-axis are more questionable. While all objects distant from the x- axis might be worth investigating, points above the cutoff should be viewed as particularly suspicious.
- 16. Conclusion of Process At the conclusion of outlier detection, an output file for each cluster containing the unique record identifier, original variables, normalized variables, principal components, normalized principal components, cluster assignments, and mahalanobis distance information can be exported to facilitate further analyses and investigations. Cluster 2 – final output file of a subset of fields: Distinguishing features of cluster 2 records: 1) New accounts (age = 1 month), 2) Very high incidence of late payments, and 3) Relatively high credit limits, particularly given the account age and late payment issues. Record AccountAge CreditLimit AdditionalAssets LatePayments model.cluster md 32430 1 2500 1 3 2 5.83E-05 65470 1 8500 1 4 2 0.002371778 78772 1 2200 0 3 2 0.000442305