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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

-Sanjay Ranka, University of Florida

-Mohammed Zaki, Rensselaer Polytechnic Institute

Introduction to Data Mining: Pearson New International Edition

• Table of Contents


• Chapter 1. Introduction


• Chapter 2. Data


• Chapter 3. Exploring Data


• Chapter 4. Classification: Basic Concepts, Decision Trees, and Model Evaluation


• Chapter 5. Classification: Alternative Techniques


• Chapter 6. Association Analysis: Basic Concepts and Algorithms


• Chapter 7. Association Analysis: Advanced Concepts


• Chapter 8. Cluster Analysis: Basic Concepts and Algorithms


• Chapter 9. Cluster Analysis: Additional Issues and Algorithms


• Chapter 10. Anomaly Detection


• Appendix B: Dimensionality Reduction


• Appendix D: Regression


• Appendix E: Optimization


• Copyright Permissions


• Index


• 1


• A


• B


• C


• D


• E


• F


• G


• H


• I


• J


• K


• L


• M


• N


• O


• P


• Q


• R


• S


• T


• U


• V


• W


• X


• Y


• Z