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

Chapter 7 Advanced Pattern Mining 279
7.1 Pattern Mining: A Road Map 279
7.2 Pattern Mining in Multilevel, Multidimensional Space 283
7.2.1 Mining Multilevel Associations 283
7.2.2 Mining Multidimensional Associations 287
7.2.3 Mining Quantitative Association Rules 289
7.2.4 Mining Rare Patterns and Negative Patterns 291
7.3 Constraint-Based Frequent Pattern Mining 294
7.3.1 Metarule-Guided Mining of Association Rules 295
7.3.2 Constraint-Based Pattern Generation: Pruning Pattern Space
and Pruning Data Space 296
7.4 Mining High-Dimensional Data and Colossal Patterns 301
7.4.1 Mining Colossal Patterns by Pattern-Fusion 302
7.5 Mining Compressed or Approximate Patterns 307
7.5.1 Mining Compressed Patterns by Pattern Clustering 308
7.5.2 Extracting Redundancy-Aware Top-k Patterns 310
7.6 Pattern Exploration and Application 313
7.6.1 Semantic Annotation of Frequent Patterns 313
7.6.2 Applications of Pattern Mining 317
7.7 Summary 319
7.8 Exercises 321
7.9 Bibliographic Notes 323

Chapter 8 Classification: Basic Concepts 327
8.1 Basic Concepts 327
8.1.1 What Is Classification? 327
8.1.2 General Approach to Classification 328
8.2 Decision Tree Induction 330
8.2.1 Decision Tree Induction 332
8.2.2 Attribute Selection Measures 336
8.2.3 Tree Pruning 344
8.2.4 Scalability and Decision Tree Induction 347
8.2.5 Visual Mining for Decision Tree Induction 348
8.3 Bayes Classification Methods 350
8.3.1 Bayes’ Theorem 350
8.3.2 Na¨ ıve Bayesian Classification 351
8.4 Rule-Based Classification 355
8.4.1 Using IF-THEN Rules for Classification 355
8.4.2 Rule Extraction from a Decision Tree 357
8.4.3 Rule Induction Using a Sequential Covering Algorithm 359