Pearson Introduction to Data Mining by Pang-Ning Tan and Michael Steinbach

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. 
 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. 

Features:
1. Provides both theoretical and practical coverage of all data mining topics
2. Includes extensive number of integrated examples and figures
 3. Offers instructor resources including solutions for exercises and complete set of lecture slides 
4. Assumes only a modest statistics or mathematics background without any requirement of database knowledge 
5. Important topics such as predictive modeling, association analysis, clustering, anomaly detection, visualization covered