Description

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

Features

• Provides both theoretical and practical coverage of all data mining topics.
• Includes extensive number of integrated examples and figures.
• Assumes only a modest statistics or mathematics background, and no database knowledge is needed.
• Topics covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization.

Table of Contents

• 1 Introduction
• 2 Data
• 3 Classification: Basic Concepts and Techniques
• 4 Association Analysis: Basic Concepts and Algorithms
• 5 Cluster Analysis: Basic Concepts and Algorithms
• 6 Classification: Alternative Techniques
• 7 Association Analysis: Advanced Concepts
• 8 Cluster Analysis: Additional Issues and Algorithms
• 9 Anomaly Detection
• 10 Avoiding False Discoveries
• Author Index