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HAN 03-toc-ix-xviii-9780123814791
Contents xvii

11.1.2 Probabilistic Model-Based Clusters 501
11.1.3 Expectation-Maximization Algorithm 505
11.2 Clustering High-Dimensional Data 508
11.2.1 Clustering High-Dimensional Data: Problems, Challenges,
and Major Methodologies 508
11.2.2 Subspace Clustering Methods 510
11.2.3 Biclustering 512
11.2.4 Dimensionality Reduction Methods and Spectral Clustering 519
11.3 Clustering Graph and Network Data 522
11.3.1 Applications and Challenges 523
11.3.2 Similarity Measures 525
11.3.3 Graph Clustering Methods 528
11.4 Clustering with Constraints 532
11.4.1 Categorization of Constraints 533
11.4.2 Methods for Clustering with Constraints 535
11.5 Summary 538
11.6 Exercises 539
11.7 Bibliographic Notes 540

Chapter 12 Outlier Detection 543
12.1 Outliers and Outlier Analysis 544
12.1.1 What Are Outliers? 544
12.1.2 Types of Outliers 545
12.1.3 Challenges of Outlier Detection 548
12.2 Outlier Detection Methods 549
12.2.1 Supervised, Semi-Supervised, and Unsupervised Methods 549
12.2.2 Statistical Methods, Proximity-Based Methods, and
Clustering-Based Methods 551
12.3 Statistical Approaches 553
12.3.1 Parametric Methods 553
12.3.2 Nonparametric Methods 558
12.4 Proximity-Based Approaches 560
12.4.1 Distance-Based Outlier Detection and a Nested Loop
Method 561
12.4.2 A Grid-Based Method 562
12.4.3 Density-Based Outlier Detection 564
12.5 Clustering-Based Approaches 567
12.6 Classification-Based Approaches 571
12.7 Mining Contextual and Collective Outliers 573
12.7.1 Transforming Contextual Outlier Detection to Conventional
Outlier Detection 573