Data Mining: Concepts and Techniques

3rd Edition

Authors: Jiawei Han Micheline Kamber Jian Pei
Hardcover ISBN: 9780123814791
eBook ISBN: 9780123814807
Imprint: Morgan Kaufmann
Published Date: 22nd June 2011
Page Count: 744
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Table of Contents

  • Dedication
  • Foreword
  • Foreword to Second Edition
  • Preface
  • Organization of the Book
  • To the Instructor
  • To the Student
  • To the Professional
  • Book Web Sites with Resources
  • Acknowledgments
  • Third Edition of the Book
  • Second Edition of the Book
  • First Edition of the Book
  • About the Authors
  • 1. Introduction
    • Publisher Summary
    • 1.1 Why Data Mining?
    • 1.2 What Is Data Mining?
    • 1.3 What Kinds of Data Can Be Mined?
    • 1.4 What Kinds of Patterns Can Be Mined?
    • 1.5 Which Technologies Are Used?
    • 1.6 Which Kinds of Applications Are Targeted?
    • 1.7 Major Issues in Data Mining
    • 1.8 Summary
    • 1.9 Exercises
    • 1.10 Bibliographic Notes
  • 2. Getting to Know Your Data
    • Publisher Summary
    • 2.1 Data Objects and Attribute Types
    • 2.2 Basic Statistical Descriptions of Data
    • 2.3 Data Visualization
    • 2.4 Measuring Data Similarity and Dissimilarity
    • 2.5 Summary
    • 2.6 Exercises
    • 2.7 Bibliographic Notes
  • 3. Data Preprocessing
    • Publisher Summary
    • 3.1 Data Preprocessing: An Overview
    • 3.2 Data Cleaning
    • 3.3 Data Integration
    • 3.4 Data Reduction
    • 3.5 Data Transformation and Data Discretization
    • 3.6 Summary
    • 3.7 Exercises
    • 3.8 Bibliographic Notes
  • 4. Data Warehousing and Online Analytical Processing
    • Publisher Summary
    • 4.1 Data Warehouse: Basic Concepts
    • 4.2 Data Warehouse Modeling: Data Cube and OLAP
    • 4.3 Data Warehouse Design and Usage
    • 4.4 Data Warehouse Implementation
    • 4.5 Data Generalization by Attribute-Oriented Induction
    • 4.6 Summary
    • 4.7 Exercises
    • Bibliographic Notes


Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

Key Features

  • Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
  • Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields
  • Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data


Data warehouse engineers, data mining professionals, database researchers, statisticians, data analysts, data modelers, and other data professionals working on data mining at the R&D and implementation levels. Upper-level undergrads and graduate students in data mining at computer science programs


No. of pages:
© Morgan Kaufmann 2012
Morgan Kaufmann
Hardcover ISBN:
eBook ISBN:


"A well-written textbook (2nd ed., 2006; 1st ed., 2001) on data mining or knowledge discovery. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data—all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners." --CHOICE

"This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers." --ACM’s Computing

"We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses." --Gregory Piatetsky, President, KDnuggets

"Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vect

About the Authors

Jiawei Han Author

Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

Affiliations and Expertise

University of Illinois, Urbana Champaign

Micheline Kamber Author

Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.

Affiliations and Expertise

Simon Fraser University, Burnaby, Canada

Jian Pei Author

Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his “contributions to the foundation, methodology and applications of data mining” and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his “contributions to data mining and knowledge discovery”. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.

Affiliations and Expertise

Simon Fraser University, Burnaby, Canada