Introduction to Data Mining, Global Edition, 2nd edition

Published by Pearson Higher Education (May 31, 2019) © 2019

  • Pang-Ning Tan Michigan State University
  • Michael Steinbach University of Minnesota
  • Vipin Kumar University of Minnesota
  • Anuj Karpatne University of Minnesota

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Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course.

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.

Teaching and Learning Experience

This program will provide a better teaching and learning experience—for you and your students. It will help:

  • Present Fundamental Concepts and Algorithms: Written for the beginner, this text provides both theoretical and practical coverage of all data mining topics.
  • Support Learning: Instructor resources include solutions for exercises and a complete set of lecture slides.
  • 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.
  • 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

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