| Foreword | |
| Preface | |
| Ch. 1 | Introduction | 1 |
| 1.1 | What Motivated Data Mining? Why Is It Important? | 1 |
| 1.2 | So, What Is Data Mining? | 5 |
| 1.3 | Data Mining - On What Kind of Data? | 10 |
| 1.4 | Data Mining Functionalities - What Kinds of Patterns Can Be Mined? | 21 |
| 1.5 | Are All of the Patterns Interesting? | 27 |
| 1.6 | Classification of Data Mining Systems | 28 |
| 1.7 | Major Issues in Data Mining | 30 |
| Ch. 2 | Data Warehouse and OLAP Technology for Data Mining | 39 |
| 2.1 | What Is a Data Warehouse? | 39 |
| 2.2 | A Multidimensional Data Model | 44 |
| 2.3 | Data Warehouse Architecture | 62 |
| 2.4 | Data Warehouse Implementation | 71 |
| 2.5 | Further Development of Data Cube Technology | 85 |
| 2.6 | From Data Warehousing to Data Mining | 93 |
| Ch. 3 | Data Preprocessing | 105 |
| 3.1 | Why Preprocess the Data? | 105 |
| 3.2 | Data Cleaning | 109 |
| 3.3 | Data Integration and Transformation | 112 |
| 3.4 | Data Reduction | 116 |
| 3.5 | Discretization and Concept Hierarchy Generation | 130 |
| Ch. 4 | Data Mining Primitives, Languages, and System Architectures | 145 |
| 4.1 | Data Mining Primitives: What Defines a Data Mining Task? | 146 |
| 4.2 | A Data Mining Query Language | 159 |
| 4.3 | Designing Graphical User Interfaces Based on a Data Mining Query Language | 170 |
| 4.4 | Architectures of Data Mining Systems | 171 |
| Ch. 5 | Concept Description: Characterization and Comparison | 179 |
| 5.1 | What Is Concept Description? | 179 |
| 5.2 | Data Generalization and Summarization-Based Characterization | 181 |
| 5.3 | Analytical Characterization: Analysis of Attribute Relevance | 194 |
| 5.4 | Mining Class Comparisons: Discriminating between Different Classes | 200 |
| 5.5 | Mining Descriptive Statistical Measures in Large Databases | 208 |
| 5.6 | Discussion | 217 |
| Ch. 6 | Mining Association Rules in Large Databases | 225 |
| 6.1 | Association Rule Mining | 226 |
| 6.2 | Mining Single-Dimensional Boolean Association Rules from Transactional Databases | 230 |
| 6.3 | Mining Multilevel Association Rules from Transaction Databases | 244 |
| 6.4 | Mining Multidimensional Association Rules from Relational Databases and Data Warehouses | 251 |
| 6.5 | From Association Mining to Correlation Analysis | 259 |
| 6.6 | Constraint-Based Association Mining | 262 |
| Ch. 7 | Classification and Prediction | 279 |
| 7.1 | What Is Classification? What Is Prediction? | 279 |
| 7.2 | Issues Regarding Classification and Prediction | 282 |
| 7.3 | Classification by Decision Tree Induction | 284 |
| 7.4 | Bayesian Classification | 296 |
| 7.5 | Classification by Backpropagation | 303 |
| 7.6 | Classification Based on Concepts from Association Rule Mining | 311 |
| 7.7 | Other Classification Methods | 314 |
| 7.8 | Prediction | 319 |
| 7.9 | Classifier Accuracy | 322 |
| Ch. 8 | Cluster Analysis | 335 |
| 8.1 | What Is Cluster Analysis? | 335 |
| 8.2 | Types of Data in Cluster Analysis | 338 |
| 8.3 | A Categorization of Major Clustering Methods | 346 |
| 8.4 | Partitioning Methods | 348 |
| 8.5 | Hierarchical Methods | 354 |
| 8.6 | Density-Based Methods | 363 |
| 8.7 | Grid-Based Methods | 370 |
| 8.8 | Model-Based Clustering Methods | 376 |
| 8.9 | Outlier Analysis | 381 |
| Ch. 9 | Mining Complex Types of Data | 395 |
| 9.1 | Multidimensional Analysis and Descriptive Mining of Complex Data Objects | 396 |
| 9.2 | Mining Spatial Databases | 405 |
| 9.3 | Mining Multimedia Databases | 412 |
| 9.4 | Mining Time-Series and Sequence Data | 418 |
| 9.5 | Mining Text Databases | 428 |
| 9.6 | Mining the World Wide Web | 435 |
| Ch. 10 | Applications and Trends in Data Mining | 451 |
| 10.1 | Data Mining Applications | 451 |
| 10.2 | Data Mining System Products and Research Prototypes | 457 |
| 10.3 | Additional Themes on Data Mining | 462 |
| 10.4 | Social Impacts of Data Mining | 472 |
| 10.5 | Trends in Data Mining | 478 |
| App. A | An Introduction to Microsoft's OLE DB for Data Mining | 485 |
| App. B | An Introduction to DBMiner | 493 |
| Bibliography | 501 |
| Index | 533 |
