Clustering

Written by two of the best-known experts in the field, Clustering is the only thoroughly comprehensive text on the subject currently available. The book looks at the full range of clustering and provides enough detail to allow users to select the method that best fits their application. A generous use of examples and references make the subject matter clear for readers of different levels, including graduate students, computer scientists, and applied mathematicians.

Rui Xu, PhD, is a Research Associate in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research interests include computational intelligence, machine learning, data mining, neural networks, pattern classification, clustering, and bioinformatics. Dr. Xu is a member of the IEEE, the IEEE Computational Intelligence Society (CIS), and Sigma Xi.

Donald C. Wunsch II, PhD, is the M.K. Finley Missouri Distinguished Professor at Missouri University of Science and Technology. His key contributions are in adaptive resonance and reinforcement learning hardware and applications, neurofuzzy regression, improved Traveling Salesman Problem heuristics, clustering, and bioinformatics. He is an IEEE Fellow, the 2005 International Neural Networks Society (INNS) President, and Senior Fellow of the INNS.

Preface	p. ix
Cluster Analysis	p. 1
Classification and Clustering	p. 1
Definition of Clusters	p. 3
Clustering Applications	p. 8
Literature of Clustering Algorithms	p. 9
Outline of the Book	p. 12
Proximity Measures	p. 15
Introduction	p. 15
Feature Types and Measurement Levels	p. 15
Definition of Proximity Measures	p. 21
Proximity Measures for Continuous Variables	p. 22
Proximity Measures for Discrete Variables	p. 26
Proximity Measures for Mixed Variables	p. 29
Summary	p. 30
Hierarchical Clustering	p. 31
Introduction	p. 31
Agglomerative Hierarchical Clustering	p. 32
Divisive Hierarchical Clustering	p. 37
Recent Advances	p. 40
Applications	p. 46
Summary	p. 61
Partitional Clustering	p. 63
Introduction	p. 63
Clustering Criteria	p. 64
K-Means Algorithm	p. 67
Mixture Density-Based Clustering	p. 73
Graph Theory-Based Clustering	p. 81
Fuzzy Clustering	p. 83
Search Techniques-Based Clustering Algorithms	p. 92
Applications	p. 99
Summary	p. 109
Neural Network-Based Clustering	p. 111
Introduction	p. 111
Hard Competitive Learning Clustering	p. 113
Soft Competitive Learning Clustering	p. 130
Applications	p. 146
Summary	p. 162
Kernel-Based Clustering	p. 163
Introduction	p. 163
Kernel Principal Component Analysis	p. 165
Squared-Error-Based Clustering with Kernel Functions	p. 167
Support Vector Clustering	p. 170
Applications	p. 175
Summary	p. 176
Sequential Data Clustering	p. 179
Introduction	p. 179
Sequence Similarity	p. 181
Indirect Sequence Clustering	p. 185
Model-Based Sequence Clustering	p. 186
Applications-Genomic and Biological Sequence Clustering	p. 201
Summary	p. 211
Large-Scale Data Clustering	p. 213
Introduction	p. 213
Random Sampling Methods	p. 216
Condensation-Based Methods	p. 219
Density-Based Methods	p. 220
Grid-Based Methods	p. 225
Divide and Conquer	p. 227
Incremental Clustering	p. 229
Applications	p. 229
Summary	p. 235
Data Visualization and High-Dimensional Data Clustering	p. 237
Introduction	p. 237
Linear Projection Algorithms	p. 239
Nonlinear Projection Algorithms	p. 244
Projected and Subspace Clustering	p. 253
Applications	p. 258
Summary	p. 260
Cluster Validity	p. 263
Introduction	p. 263
External Criteria	p. 265
Internal Criteria	p. 267
Relative Criteria	p. 268
Summary	p. 277
Concluding Remarks	p. 279
Problems	p. 283
References	p. 293
Author Index	p. 331
Subject Index	p. 341
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