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 topics2. Includes extensive number of integrated examples and figures3. Offers instructor resources including solutions for exercises and complete set of lecture slides4. Assumes only a modest statistics or mathematics background without any requirement of database knowledge5. Important topics such as predictive modeling, association analysis, clustering, anomaly detection, visualization coveredContentsChapter 1 IntroductionChapter 2 DataChapter 3 Exploring DataChapter 4 Classification: Basic Concepts, Decision Trees, and Model EvaluationChapter 5 Classification: Alternative TechniquesChapter 6 Association Analysis: Basic Concepts and AlgorithmsChapter 7 Association Analysis: Advanced ConceptsChapter 8 Cluster Analysis: Basic Concepts and AlgorithmsChapter 9 Cluster Analysis: Additional Issues and AlgorithmsChapter 10 Anomaly Detection
About the author
Pang-Ning Tan, Michigan State UniversityMichael Steinbach, University of MinnesotaVipin Kumar, University of Minnesota
