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Introduction to Data Mining is a comprehensive book for computer science undergraduates and professionals taking up a course in the computational process of discovering patterns in large sets of data. The book introduces students to the concepts of data mining, covering practical and theoretical aspects of the subject. It contains a large number of examples to illustrate the concepts, making it easier for students to put the theory in an application-based setting. In addition, the book includes instructor resources, giving lecturers access to online resources for exercises and complete set of lecture slides. The book is indispensable to all computer science engineers and statisticians. About the Authors Pang-Ning Tan is an Associate Professor of Computer Science and Engineering at the Michigan State University. A physics graduate, he obtained his Ph.D. in Computer Science from the University of Minnesota. He is well known in the data mining research circles for his contributions towards association analysis, anomaly detection, and cluster analysis and mainly predictive modeling. Michael Steinbach is a Research Associate at the Department of Computer Science and Engineering, University of Minnesota. A statistics graduate, he earned his Ph.D. in Computer Science from the University of Minnesota. Vipin Kumar is the William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota. A graduate of Indian Institute of Technology Roorkee, he pursued his M.E. in Electronics Engineering from Philips International Institute, Eindhoven, Netherlands and a Ph.D. in Computer Science from University of Maryland, College Park. He is well known for his research in the fields of data mining, high-performance computing, and their applications in Climate/Ecosystems and Biomedical domains.