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Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability) [Paperback]

Guojun Gan , Chaoqun Ma , Jianhong Wu

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Book Description

May 30, 2007 0898716233 978-0898716238
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. Audience The following groups will find this book a valuable tool and reference: applied statisticians; engineers and scientists using data analysis; researchers in pattern recognition, artificial intelligence, machine learning, and data mining; and applied mathematicians. Instructors can also use it as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining. Contents Preface; Chapter 1: Data Clustering; Chapter 2: Data Types; Chapter 3: Scale Conversion; Chapter 4: Data Standardizatin and Transformation; Chapter 5: Data Visualization; Chapter 6: Similarity and Dissimilarity Measures; Chapter 7: Hierarchical Clustering Techniques; Chapter 8: Fuzzy Clustering Algorithms; Chapter 9: Center Based Clustering Algorithms; Chapter 10: Search Based Clustering Algorithms; Chapter 11: Graph Based Clustering Algorithms; Chatper 12: Grid Based Clustering Algorithms; Chapter 13: Density Based Clustering Algorithms; Chapter 14: Model Based Clustering Algorithms; Chapter 15: Subspace Clustering; Chapter 16: Miscellaneous Algorithms; Chapter 17: Evaluation of Clustering Algorithms; Chapter 18: Clustering Gene Expression Data; Chapter 19: Data Clustering in MATLAB; Chapter 20: Clustering in C/C++; Appendix A: Some Clustering Algorithms; Appendix B: Thekd-tree Data Structure; Appendix C: MATLAB Codes; Appendix D: C++ Codes; Subject Index; Author Index

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Editorial Reviews

Book Description

This book enables readers and users to easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. A valuable tool and reference for applied statisticians; engineers, scientists and applied mathematicians.

About the Author

Guojun Gan is a Ph.D. candidate in the Department of Mathematics and Statistics at York University, Ontario, Canada. His research interests include data mining and data clustering, and he is the coauthor of algorithms for clustering categorical data and for subspace/projective clustering of high-dimensional data. Chaoqun Ma is Professor and the Deputy Dean of the College of Business Administration at Hunan University, People s Republic of China. His main research areas are data mining, financial engineering, and risk management. Jianhong Wu is a Senior Canada Research Chair in Applied Mathematics at York University, Ontario, Canada. He is the author or coauthor of over 200 peer-reviewed publications and six monographs in the areas of nonlinear dynamical systems, delay differential equations, mathematical biology and epidemiology, neural networks, and pattern formation and recognition.

Product Details

  • Series: ASA-SIAM Series on Statistics and Applied Probability (Book 20)
  • Paperback: 466 pages
  • Publisher: SIAM, Society for Industrial and Applied Mathematics (May 30, 2007)
  • Language: English
  • ISBN-10: 0898716233
  • ISBN-13: 978-0898716238
  • Product Dimensions: 9.8 x 7.4 x 0.9 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Amazon Best Sellers Rank: #1,515,227 in Books (See Top 100 in Books)

More About the Author

Guojun Gan holds a PhD. degree and a MS degree in applied mathematics from York University, Toronto, ON, Canada and a BS degree in computational mathematics and its applied software from Jilin University, Changchun, Jilin, P.R. China. Currently, Guojun Gan is a Director, Research & Development at the Global Variable Annuity Hedging Department of Manulife Financial, where he is responsible for improving, exploring, and developing mathematical models to support the global variable annuity hedging programs. During his PhD study at York University, Guojun has published one book and several papers on data clustering. While working in industry, Guojun continued to work on scholarly projects in his free time and published two books.

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