Data Mining–Concepts and Techniques by Han, Jiawei -- Read -- Imperial Library of Trantor

Index

Cover Image Table of Contents Front Matter Copyright Dedication Foreword Foreword to Second Edition Preface Acknowledgments About the Authors 1. Introduction

1.1. Why Data Mining? 1.2. What Is Data Mining? 1.3. What Kinds of Data Can Be Mined? 1.4. What Kinds of Patterns Can Be Mined? 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 2.4. Measuring Data Similarity and Dissimilarity 2.5. Summary 2.6. Exercises 2.7. Bibliographic Notes

3. Data Preprocessing

3.1. Data Preprocessing: An Overview 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 4.3. Data Warehouse Design and Usage 4.4. Data Warehouse Implementation 4.5. Data Generalization by Attribute-Oriented Induction 4.6. Summary 4.7. Exercises

5. Data Cube Technology

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 6.3. Which Patterns Are Interesting?—Pattern Evaluation Methods 6.4. Summary 6.5. Exercises 6.6. Bibliographic Notes

7. Advanced Pattern Mining

7.1. Pattern Mining: A Road Map 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 8.2. Decision Tree Induction 8.3. Bayes Classification Methods 8.4. Rule-Based Classification 8.5. Model Evaluation and Selection 8.6. Techniques to Improve Classification Accuracy 8.7. Summary 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

10.1. Cluster Analysis 10.2. Partitioning Methods 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

11.1. Probabilistic Model-Based Clustering 11.2. Clustering High-Dimensional Data 11.3. Clustering Graph and Network Data 11.4. Clustering with Constraints 11.6. Exercises 11.7. Bibliographic Notes

12. Outlier Detection

12.1. Outliers and Outlier Analysis 12.2. Outlier Detection Methods 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

13.1. Mining Complex Data Types 13.2. Other Methodologies of Data Mining 13.3. Data Mining Applications 13.4. Data Mining and Society 13.5. Data Mining Trends 13.6. Summary 13.7. Exercises 13.8. Bibliographic Notes

Bibliography Index

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