Introduction to Data Mining

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 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.

Key Features

• Provides both theoretical and practical coverage of all data mining topics.
• Includes extensive number of integrated examples and figures.
• Offers instructor resources including solutions for exercises and complete set of lecture slides.
• 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.

Reviews

“This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.” – Sanjay Ranka, University of Florida

“In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules).” – Mohammed Zaki, Rensselaer Polytechnic Institute