Clustering

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Clustering
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Author : Rui Xu
language : en
Publisher: John Wiley & Sons
Release Date : 2008-11-03
Clustering written by Rui Xu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-03 with Mathematics categories.
This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.
Data Clustering
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2016-03-29
Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-29 with Business & Economics categories.
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Algorithms For Fuzzy Clustering
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Author : Sadaaki Miyamoto
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-15
Algorithms For Fuzzy Clustering written by Sadaaki Miyamoto and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-15 with Computers categories.
Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
Constrained Clustering
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Author : Sugato Basu
language : en
Publisher: CRC Press
Release Date : 2008-08-18
Constrained Clustering written by Sugato Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-18 with Business & Economics categories.
This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering. With contributions from industrial researchers and leading academic experts who pioneered the field, it provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications. The book presents various types of constraints for clustering and describes useful variations of the standard problem of clustering under constraints. It also demonstrates the application of clustering with constraints to relational, bibliographic, and video data.
Clustering Techniques For Image Segmentation
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Author : Fasahat Ullah Siddiqui
language : en
Publisher: Springer Nature
Release Date : 2021-10-29
Clustering Techniques For Image Segmentation written by Fasahat Ullah Siddiqui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Technology & Engineering categories.
This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM). The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. Showcases major clustering techniques, detailing their advantages and shortcomings; Includes several methods for evaluating the performance of segmentation techniques; Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.
Clustering Challenges In Biological Networks
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Author : W Art Chaovalitwongse
language : en
Publisher: World Scientific
Release Date : 2009-02-11
Clustering Challenges In Biological Networks written by W Art Chaovalitwongse and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-11 with Science categories.
This volume presents a collection of papers dealing with various aspects of clustering in biological networks and other related problems in computational biology. It consists of two parts, with the first part containing surveys of selected topics and the second part presenting original research contributions. This book will be a valuable source of material to faculty, students, and researchers in mathematical programming, data analysis and data mining, as well as people working in bioinformatics, computer science, engineering, and applied mathematics. In addition, the book can be used as a supplement to any course in data mining or computational/systems biology.
Advances In K Means Clustering
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Author : Junjie Wu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-09
Advances In K Means Clustering written by Junjie Wu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-09 with Computers categories.
Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
Data Clustering
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Author : Guojun Gan
language : en
Publisher: SIAM
Release Date : 2007-07-12
Data Clustering written by Guojun Gan and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-12 with Mathematics categories.
Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.
Data Clustering
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Author : Guojun Gan
language : en
Publisher: SIAM
Release Date : 2007-01-01
Data Clustering written by Guojun Gan and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-01 with Mathematics categories.
Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB programming languages.
Cluster Analysis
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Author : Brian S. Everitt
language : en
Publisher: Taylor & Francis
Release Date : 2001
Cluster Analysis written by Brian S. Everitt and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
Cluster analysis comprises a range of methods of classifying multivariate data into subgroups and these techniques are widely applicable. This new edition incorporates material covering developing areas such as Bayesian statistics & neural networks.