Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)

Table of Contents......Page 5
Preface......Page 17
Organization of the Book......Page 18
To the Instructor......Page 20
To the Professional......Page 21
Book Websites with Resources......Page 22
Acknowledgments for the Second Edition of the Book......Page 23
1.1 What Motivated Data Mining? Why Is It Important?......Page 25
1.2 So, What Is Data Mining?......Page 29
1.3 Data Mining—On What Kind of Data?......Page 33
1.4 Data Mining Functionalities—What Kinds of Patterns Can Be Mined?......Page 45
1.5 Are All of the Patterns Interesting?......Page 51
1.6 Classification of Data Mining Systems......Page 53
1.7 Data Mining Task Primitives......Page 55
1.8 Integration of a Data Mining System with a Database or Data Warehouse System......Page 58
1.9 Major Issues in Data Mining......Page 60
1.10 Summary......Page 63
Exercises......Page 64
Bibliographic Notes......Page 66
2 Data Preprocessing......Page 70
2.1 Why Preprocess the Data?......Page 71
2.2 Descriptive Data Summarization......Page 74
2.3 Data Cleaning......Page 84
2.4 Data Integration and Transformation......Page 90
2.5 Data Reduction......Page 95
2.6 Data Discretization and Concept Hierarchy Generation......Page 109
Exercises......Page 120
Bibliographic Notes......Page 124
3.1 What Is a Data Warehouse?......Page 127
3.2 A Multidimensional Data Model......Page 132
3.3 Data Warehouse Architecture......Page 149
3.4 Data Warehouse Implementation......Page 159
3.5 From Data Warehousing to Data Mining......Page 168
3.6 Summary......Page 172
Exercises......Page 174
Bibliographic Notes......Page 176
4.1 Efficient Methods for Data Cube Computation......Page 178
4.2 Further Development of Data Cube and OLAP Technology......Page 210
4.3 Attribute-Oriented Induction—An Alternative Method for Data Generalization and Concept Description......Page 219
4.4 Summary......Page 239
Exercises......Page 240
Bibliographic Notes......Page 244
5.1 Basic Concepts and a Road Map......Page 247
5.2 Efficient and Scalable Frequent Itemset Mining Methods......Page 254
5.3 Mining Various Kinds of Association Rules......Page 270
5.4 From Association Mining to Correlation Analysis......Page 279
5.5 Constraint-Based Association Mining......Page 285
5.6 Summary......Page 292
Exercises......Page 294
Bibliographic Notes......Page 300
6.1 What Is Classification? What Is Prediction?......Page 304
6.2 Issues Regarding Classification and Prediction......Page 308
6.3 Classification by Decision Tree Induction......Page 310
6.4 Bayesian Classification......Page 329
6.5 Rule-Based Classification......Page 337
6.6 Classification by Backpropagation......Page 346
6.7 Support Vector Machines......Page 356
6.8 Associative Classification: Classification by Association Rule Analysis......Page 363
6.9 Lazy Learners (or Learning from Your Neighbors)......Page 366
6.10 Other Classification Methods......Page 370
6.11 Prediction......Page 373
6.12 Accuracy and Error Measures......Page 378
6.13 Evaluating the Accuracy of a Classifier or Predictor......Page 382
6.14 Ensemble Methods—Increasing the Accuracy......Page 385
6.15 Model Selection......Page 389
6.16 Summary......Page 392
Exercises......Page 394
Bibliographic Notes......Page 397
7.1 What Is Cluster Analysis?......Page 402
7.2 Types of Data in Cluster Analysis......Page 405
7.3 A Categorization of Major Clustering Methods......Page 417
7.4 Partitioning Methods......Page 420
7.5 Hierarchical Methods......Page 427
7.6 Density-Based Methods......Page 437
7.7 Grid-Based Methods......Page 443
7.8 Model-Based Clustering Methods......Page 448
7.9 Clustering High-Dimensional Data......Page 453
7.10 Constraint-Based Cluster Analysis......Page 463
7.11 Outlier Analysis......Page 470
7.12 Summary......Page 479
Exercises......Page 480
Bibliographic Notes......Page 483
8 Mining Stream, Time-Series, and Sequence Data......Page 486
8.1 Mining Data Streams......Page 487
8.2 Mining Time-Series Data......Page 508
8.3 Mining Sequence Patterns in Transactional Databases......Page 517
8.4 Mining Sequence Patterns in Biological Data......Page 532
8.5 Summary......Page 546
Exercises......Page 547
Bibliographic Notes......Page 550
9.1 Graph Mining......Page 554
9.2 Social Network Analysis......Page 574
9.3 Multirelational Data Mining......Page 590
9.4 Summary......Page 603
Exercises......Page 605
Bibliographic Notes......Page 606
10.1 Multidimensional Analysis and Descriptive Mining of Complex Data Objects......Page 609
10.2 Spatial Data Mining......Page 618
10.3 Multimedia Data Mining......Page 625
10.4 Text Mining......Page 632
10.5 Mining the World Wide Web......Page 646
10.6 Summary......Page 659
Exercises......Page 660
Bibliographic Notes......Page 663
11.1 Data Mining Applications......Page 667
11.2 Data Mining System Products and Research Prototypes......Page 678
11.3 Additional Themes on Data Mining......Page 683
11.4 Social Impacts of Data Mining......Page 693
11.5 Trends in Data Mining......Page 699
11.6 Summary......Page 702
Exercises......Page 703
Bibliographic Notes......Page 705
Appendix: An Introduction to Microsoft’s OLE DB for Data Mining......Page 709
A.1 Model Creation......Page 711
A.2 Model Training......Page 713
A.3 Model Prediction and Browsing......Page 715
Bibliography......Page 721