• Dedication • Foreword • Foreword to Second Edition • Preface • Organization of the Book • To the Instructor • To the Student • To the Professional • Book Web Sites with Resources • Acknowledgments • Third Edition of the Book • Second Edition of the Book • First Edition of the Book • About the Authors • 1. Introduction • Publisher Summary • 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? Download The Warriors For Pc Iso Download.

Jiawei Han and Micheline Kamber Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers, August 2000. ISBN 1-55860-489-8. Table of Contents in PDF. Errata on the first and second printings of the book. Errata on the 3rd. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD).

• 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 • Publisher Summary • 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 • Publisher Summary • 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 • Publisher Summary • 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 • Bibliographic Notes • 5. Data Cube Technology • Publisher Summary • 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: Basic Concepts and Methods • Publisher Summary • 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 • Publisher Summary • 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: Basic Concepts • Publisher Summary • 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: Advanced Methods • Publisher Summary • 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 • Summary • 9.9 Exercises • 9.10 Bibliographic Notes • 10. Cluster Analysis: Basic Concepts and Methods • Publisher Summary • 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 • Publisher Summary • 11.1 Probabilistic Model-Based Clustering • 11.2 Clustering High-Dimensional Data • 11.3 Clustering Graph and Network Data • 11.4 Clustering with Constraints • Summary • 11.6 Exercises • 11.7 Bibliographic Notes • 12. Outlier Detection • Publisher Summary • 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 • Publisher Summary • 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. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications.