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xvi Contents HAN 03-toc-ix-xviii-9780123814791 2011/6/1 3:32 Page xvi #8

9.7.2 Semi-Supervised Classification 432
9.7.3 Active Learning 433
9.7.4 Transfer Learning 434
9.8 Summary 436
9.9 Exercises 438
9.10 Bibliographic Notes 439
Chapter 10 Cluster Analysis: Basic Concepts and Methods 443
10.1 Cluster Analysis 444
10.1.1 What Is Cluster Analysis? 444
10.1.2 Requirements for Cluster Analysis 445
10.1.3 Overview of Basic Clustering Methods 448
10.2 Partitioning Methods 451
10.2.1 k-Means: A Centroid-Based Technique 451
10.2.2 k-Medoids: A Representative Object-Based Technique 454
10.3 Hierarchical Methods 457
10.3.1 Agglomerative versus Divisive Hierarchical Clustering 459
10.3.2 Distance Measures in Algorithmic Methods 461
10.3.3 BIRCH: Multiphase Hierarchical Clustering Using Clustering
Feature Trees 462
10.3.4 Chameleon: Multiphase Hierarchical Clustering Using Dynamic
Modeling 466
10.3.5 Probabilistic Hierarchical Clustering 467
10.4 Density-Based Methods 471
10.4.1 DBSCAN: Density-Based Clustering Based on Connected
Regions with High Density 471
10.4.2 OPTICS: Ordering Points to Identify the Clustering Structure 473
10.4.3 DENCLUE: Clustering Based on Density Distribution Functions 476
10.5 Grid-Based Methods 479
10.5.1 STING: STatistical INformation Grid 479
10.5.2 CLIQUE: An Apriori-like Subspace Clustering Method 481
10.6 Evaluation of Clustering 483
10.6.1 Assessing Clustering Tendency 484
10.6.2 Determining the Number of Clusters 486
10.6.3 Measuring Clustering Quality 487
10.7 Summary 490
10.8 Exercises 491
10.9 Bibliographic Notes 494

Chapter 11 Advanced Cluster Analysis 497
11.1 Probabilistic Model-Based Clustering 497
11.1.1 Fuzzy Clusters 499