Data Mining. Concepts and Techniques, 3rd Edition
HAN 03-toc-ix-xviii-9780123814791
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- Bu səhifədəki naviqasiya:
- Summary 436
- Partitioning Methods 451
- Density-Based Methods 471
- Grid-Based Methods 479
- Summary 490
- Clustering High-Dimensional Data 508
- Clustering Graph and Network Data 522
- Summary 538
- Outlier Detection Methods 549
- Statistical Approaches 553
- Clustering-Based Approaches 567
- Outlier Detection in High-Dimensional Data 576
- Summary 581
- Other Methodologies of Data Mining 598
- Data Mining and Society 618
- Bibliographic Notes 628 Bibliography 633 Index 673
HAN 03-toc-ix-xviii-9780123814791 2011/6/1 3:32 Page xvi #8 xvi Contents 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'>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
HAN 03-toc-ix-xviii-9780123814791 2011/6/1 3:32 Page xvii #9 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
HAN 03-toc-ix-xviii-9780123814791 2011/6/1 3:32 Page xviii #10 xviii Contents 12.7.2 Modeling Normal Behavior with Respect to Contexts 574 12.7.3 Mining Collective Outliers 575 12.8
Outlier Detection in High-Dimensional Data 576 12.8.1 Extending Conventional Outlier Detection 577 12.8.2 Finding Outliers in Subspaces 578 12.8.3 Modeling High-Dimensional Outliers 579 12.9
Summary 581 12.10 Exercises 582 12.11 Bibliographic Notes 583 Chapter 13 Data Mining Trends and Research Frontiers 585 13.1
Mining Complex Data Types 585 13.1.1 Mining Sequence Data: Time-Series, Symbolic Sequences, and Biological Sequences 586
13.1.2 Mining Graphs and Networks 591
13.1.3 Mining Other Kinds of Data 595
13.2 Other Methodologies of Data Mining 598 13.2.1 Statistical Data Mining 598 13.2.2 Views on Data Mining Foundations 600 13.2.3 Visual and Audio Data Mining 602 13.3
Data Mining Applications 607 13.3.1 Data Mining for Financial Data Analysis 607 13.3.2 Data Mining for Retail and Telecommunication Industries 609 13.3.3 Data Mining in Science and Engineering 611 13.3.4 Data Mining for Intrusion Detection and Prevention 614 13.3.5 Data Mining and Recommender Systems 615 13.4
Data Mining and Society 618 13.4.1 Ubiquitous and Invisible Data Mining 618 13.4.2 Privacy, Security, and Social Impacts of Data Mining 620 13.5
Data Mining Trends 622 13.6
Summary 625 13.7
Exercises 626 13.8
Bibliographic Notes 628 Bibliography 633 Index 673 Yüklə 7,95 Mb. Dostları ilə paylaş:
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