Clustering For Data Mining
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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.
Classification Clustering And Data Analysis
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Author : Krzystof Jajuga
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Classification Clustering And Data Analysis written by Krzystof Jajuga 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 2012-12-06 with Computers categories.
The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over lapping) groups, we find in particular the following areas: • Clustering • Cluster validation • Discrimination • Multivariate data analysis • Statistical methods • Symbolic data analysis • Consensus trees and phylogeny • Regression trees • Neural networks and genetic algorithms • Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.
Clustering For Data Mining
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Author : Boris Mirkin
language : en
Publisher: CRC Press
Release Date : 2005-04-29
Clustering For Data Mining written by Boris Mirkin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-29 with Business & Economics categories.
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that wou
Cluster Analysis For Data Mining And System Identification
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Author : János Abonyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-22
Cluster Analysis For Data Mining And System Identification written by János Abonyi 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 2007-06-22 with Mathematics categories.
The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.
Data Mining Southeast Asia Edition
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Author : Jiawei Han
language : en
Publisher: Elsevier
Release Date : 2006-04-06
Data Mining Southeast Asia Edition written by Jiawei Han and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Computers categories.
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Cluster Analysis And Data Mining
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Author : Ronald S. King
language : en
Publisher: Mercury Learning and Information
Release Date : 2015-05-12
Cluster Analysis And Data Mining written by Ronald S. King and has been published by Mercury Learning and Information this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-12 with Computers categories.
Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]. FEATURES *Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis *Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) *Contains separate chapters on JAN and the clustering of categorical data *Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.
Clustering For Data Mining
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Author : Boris Mirkin
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2005-04-29
Clustering For Data Mining written by Boris Mirkin and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-29 with Business & Economics categories.
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues that are beyond the scope of most texts, and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference.
Classification Clustering And Data Mining Applications
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Author : David Banks
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-01-07
Classification Clustering And Data Mining Applications written by David Banks 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 2011-01-07 with Language Arts & Disciplines categories.
Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Data Clustering
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2018-09-03
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 2018-09-03 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.
Advanced Data Mining And Applications
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Author : Xue Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-07-26
Advanced Data Mining And Applications written by Xue Li 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 2006-07-26 with Computers categories.
Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.