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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.
编者：RUI XU DONALD C. WUNSCH, II
目录：
PREFACE ix
1. CLUSTER ANALYSIS 1
1.1. Classifi cation and Clustering / 1
1.2. Defi nition of Clusters / 3
1.3. Clustering Applications / 8
1.4. Literature of Clustering Algorithms / 9
1.5. Outline of the Book / 12
2. PROXIMITY MEASURES 15
2.1. Introduction / 15
2.2. Feature Types and Measurement Levels / 15
2.3. Defi nition of Proximity Measures / 21
2.4. Proximity Measures for Continuous Variables / 22
2.5. Proximity Measures for Discrete Variables / 26
2.6. Proximity Measures for Mixed Variables / 29
2.7. Summary / 30
3. HIERARCHICAL CLUSTERING 31
3.1. Introduction / 31
3.2. Agglomerative Hierarchical Clustering / 32
3.3. Divisive Hierarchical Clustering / 37
3.4. Recent Advances / 40
3.5. Applications / 46
3.6. Summary / 61
4. PARTITIONAL CLUSTERING 63
4.1. Introduction / 63
4.2. Clustering Criteria / 64
4.3. K-Means Algorithm / 67
4.4. Mixture Density-Based Clustering / 73
4.5. Graph Theory-Based Clustering / 81
4.6. Fuzzy Clustering / 83
4.7. Search Techniques-Based Clustering Algorithms / 92
4.8. Applications / 99
4.9. Summary / 109
5. NEURAL NETWORK–BASED CLUSTERING 111
5.1. Introduction / 111
5.2. Hard Competitive Learning Clustering / 113
5.3. Soft Competitive Learning Clustering / 130
5.4. Applications / 146
5.5. Summary / 162
6. KERNEL-BASED CLUSTERING 163
6.1. Introduction / 163
6.2. Kernel Principal Component Analysis / 165
6.3. Squared-Error-Based Clustering with Kernel Functions / 167
6.4. Support Vector Clustering / 170
6.5. Applications / 175
6.6. Summary / 176
7. SEQUENTIAL DATA CLUSTERING 179
7.1. Introduction / 179
7.2. Sequence Similarity / 181
7.3. Indirect Sequence Clustering / 185
7.4. Model-Based Sequence Clustering / 186
7.5. Applications—Genomic and Biological Sequence
Clustering / 201
7.6. Summary / 211
8. LARGE-SCALE DATA CLUSTERING 213
8.1. Introduction / 213
8.2. Random Sampling Methods / 216
8.3. Condensation-Based Methods / 219
8.4. Density-Based Methods / 220
8.5. Grid-Based Methods / 225
8.6. Divide and Conquer / 227
8.7. Incremental Clustering / 229
8.8. Applications / 229
8.9. Summary / 235
9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA
CLUSTERING 237
9.1. Introduction / 237
9.2. Linear Projection Algorithms / 239
9.3. Nonlinear Projection Algorithms / 244
9.4. Projected and Subspace Clustering / 253
9.5. Applications / 258
9.6. Summary / 260
10. CLUSTER VALIDITY 263
10.1. Introduction / 263
10.2. External Criteria / 265
10.3. Internal Criteria / 267
10.4. Relative Criteria / 268
10.5. Summary / 277
11. CONCLUDING REMARKS 279
PROBLEMS 283
REFERENCES 293
AUTHOR INDEX 331
SUBJECT INDEX 341
Clustering.By RUI XU.pdf
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数据挖掘领域相关丛书系列导航：
之一：Computational Methods of Feature Selection
http://www.pinggu.org/bbs/thread-580906-1-1.html
之二：Encyclopedia Of Data Warehousing and Mining（2nd edition）
http://www.pinggu.org/bbs/thread-581974-1-1.html
之三：Pattern Recognition and Machine Learning
http://www.pinggu.org/bbs/thread-584359-1-1.html
之四：Clustering
http://www.pinggu.org/bbs/thread-472052-1-1.html
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