数据挖掘概念与技术第三版（英文版）

Table of Contents

Cover Image

Front Matter

Copyright

Dedication

Foreword

Foreword to Second Edition

Preface

Acknowledgments

1.5. Which Technologies Are Used?

1.6. Which Kinds of Applications Are Targeted?

1.7. Major Issues in Data Mining

1.8. Summary

1.9. Exercises

1.10. Bibliographic Notes

2. Getting to Know Your Data

2.1. Data Objects and Attribute Types

2.2. Basic Statistical Descriptions of Data

2.3. Data Visualization

3.2. Data Cleaning

3.3. Data Integration

3.4. Data Reduction

3.5. Data Transformation and Data Discretization

3.6. Summary

3.7. Exercises

3.8. Bibliographic Notes

4. Data Warehousing and Online Analytical Processing

4.1. Data Warehouse: Basic Concepts

4.2. Data Warehouse Modeling: Data Cube and OLAP

5.1. Data Cube Computation: Preliminary Concepts

5.2. Data Cube Computation Methods

5.3. Processing Advanced Kinds of Queries by Exploring Cube Technology

5.4. Multidimensional Data Analysis in Cube Space

5.5. Summary

5.6. Exercises

5.7. Bibliographic Notes

6. Mining Frequent Patterns, Associations, and Correlations

6.1. Basic Concepts

6.2. Frequent Itemset Mining Methods

7.2. Pattern Mining in Multilevel, Multidimensional Space

7.3. Constraint-Based Frequent Pattern Mining

7.4. Mining High-Dimensional Data and Colossal Patterns

7.5. Mining Compressed or Approximate Patterns

7.6. Pattern Exploration and Application

7.7. Summary

7.8. Exercises

7.9. Bibliographic Notes

8. Classification

8.1. Basic Concepts

8.8. Exercises

8.9. Bibliographic Notes

9. Classification

9.1. Bayesian Belief Networks

9.2. Classification by Backpropagation

9.3. Support Vector Machines

9.4. Classification Using Frequent Patterns

9.5. Lazy Learners (or Learning from Your Neighbors)

9.6. Other Classification Methods

9.7. Additional Topics Regarding Classification

9.9. Exercises

9.10. Bibliographic Notes

10. Cluster Analysis

10.3. Hierarchical Methods

10.4. Density-Based Methods

10.5. Grid-Based Methods

10.6. Evaluation of Clustering

10.7. Summary

10.8. Exercises

10.9. Bibliographic Notes

11. Advanced Cluster Analysis

11.2. Clustering High-Dimensional Data

11.3. Clustering Graph and Network Data

12.3. Statistical Approaches

12.4. Proximity-Based Approaches

12.5. Clustering-Based Approaches

12.6. Classification-Based Approaches

12.7. Mining Contextual and Collective Outliers

12.8. Outlier Detection in High-Dimensional Data

12.9. Summary

12.10. Exercises

12.11. Bibliographic Notes

13. Data Mining Trends and Research Frontiers

13.6. Summary