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Data Mining Concepts and Techniques 3rd Edition [PDF].pdf

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    Front Cover
    Data Mining: Concepts and Techniques
    Copyright
    Dedication
    Table of Contents
    Foreword
    Foreword to Second Edition
    Preface
    Acknowledgments
    About the Authors
    Chapter 1. Introduction
    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
    Chapter 2. Getting to Know Your Data
    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
    Chapter 3. Data Preprocessing
    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
    Chapter 4. Data Warehousing and Online Analytical Processing
    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
    4.8 Bibliographic Notes
    Chapter 5. Data Cube Technology
    5.1 Data Cube Computation: Preliminary Concepts
    5.2 Data Cube Computation Methods
    5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology
    5.4 Multidimensional Data Analysis in Cube Space
    5.5 Summary
    5.6 Exercises
    5.7 Bibliographic Notes
    Chapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
    6.1 Basic Concepts
    6.2 Frequent Itemset Mining Methods
    6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
    6.4 Summary
    6.5 Exercises
    6.6 Bibliographic Notes
    Chapter 7. Advanced Pattern Mining
    7.1 Pattern Mining: A Road Map
    7.2 Pattern Mining in Multilevel, Multidimensional Space
    7.3 Constraint-Based Frequent Pattern Mining
    7.4 Mining High-Dimensional Data and Colossal Patterns
    7.5 Mining Compressed or Approximate Patterns
    7.6 Pattern Exploration and Application
    7.7 Summary
    7.8 Exercises
    7.9 Bibliographic Notes
    Chapter 8. Classification: Basic Concepts
    8.1 Basic Concepts
    8.2 Decision Tree Induction
    8.3 Bayes Classification Methods
    8.4 Rule-Based Classification
    8.5 Model Evaluation and Selection
    8.6 Techniques to Improve Classification Accuracy
    8.7 Summary
    8.8 Exercises
    8.9 Bibliographic Notes
    Chapter 9. Classification: Advanced Methods
    9.1 Bayesian Belief Networks
    9.2 Classification by Backpropagation
    9.3 Support Vector Machines
    9.4 Classification Using Frequent Patterns
    9.5 Lazy Learners (or Learning from Your Neighbors)
    9.6 Other Classification Methods
    9.7 Additional Topics Regarding Classification
    9.8 Summary
    9.9 Exercises
    9.10 Bibliographic Notes
    Chapter 10. Cluster Analysis: Basic Concepts and Methods
    10.1 Cluster Analysis
    10.2 Partitioning Methods
    10.3 Hierarchical Methods
    10.4 Density-Based Methods
    10.5 Grid-Based Methods
    10.6 Evaluation of Clustering
    10.7 Summary
    10.8 Exercises
    10.9 Bibliographic Notes
    Chapter 11. Advanced Cluster Analysis
    11.1 Probabilistic Model-Based Clustering
    11.2 Clustering High-Dimensional Data
    11.3 Clustering Graph and Network Data
    11.4 Clustering with Constraints
    11.5 Summary
    11.6 Exercises
    11.7 Bibliographic Notes
    Chapter 12. Outlier Detection
    12.1 Outliers and Outlier Analysis
    12.2 Outlier Detection Methods
    12.3 Statistical Approaches
    12.4 Proximity-Based Approaches
    12.5 Clustering-Based Approaches
    12.6 Classification-Based Approaches
    12.7 Mining Contextual and Collective Outliers
    12.8 Outlier Detection in High-Dimensional Data
    12.9 Summary
    12.10 Exercises
    12.11 Bibliographic Notes
    Chapter 13. Data Mining Trends and Research Frontiers
    13.1 Mining Complex Data Types
    13.2 Other Methodologies of Data Mining
    13.3 Data Mining Applications
    13.4 Data Mining and Society
    13.5 Data Mining Trends
    13.6 Summary
    13.7 Exercises
    13.8 Bibliographic Notes
    Bibliography
    Index
    Data Mining Third Edition
    The Morgan Kaufmann Series in Data Management Systems (Selected Titles) Joe Celko’s Data, Measurements, and Standards in SQL Joe Celko Information Modeling and Relational Databases, 2nd Edition Terry Halpin, Tony Morgan Joe Celko’s Thinking in Sets Joe Celko Business Metadata Bill Inmon, Bonnie O’Neil, Lowell Fryman Unleashing Web 2.0 Gottfried Vossen, Stephan Hagemann Enterprise Knowledge Management David Loshin The Practitioner’s Guide to Data Quality Improvement David Loshin Business Process Change, 2nd Edition Paul Harmon IT Manager’s Handbook, 2nd Edition Bill Holtsnider, Brian Jaffe Joe Celko’s Puzzles and Answers, 2nd Edition Joe Celko Architecture and Patterns for IT Service Management, 2nd Edition, Resource Planning and Governance Charles Betz Joe Celko’s Analytics and OLAP in SQL Joe Celko Data Preparation for Data Mining Using SAS Mamdouh Refaat Querying XML: XQuery, XPath, and SQL/ XML in Context Jim Melton, Stephen Buxton Data Mining: Concepts and Techniques, 3rd Edition Jiawei Han, Micheline Kamber, Jian Pei Database Modeling and Design: Logical Design, 5th Edition Toby J. Teorey, Sam S. Lightstone, Thomas P. Nadeau, H. V. Jagadish Foundations of Multidimensional and Metric Data Structures Hanan Samet Joe Celko’s SQL for Smarties: Advanced SQL Programming, 4th Edition Joe Celko Moving Objects Databases Ralf Hartmut G¨uting, Markus Schneider Joe Celko’s SQL Programming Style Joe Celko Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration Earl Cox
    Data Modeling Essentials, 3rd Edition Graeme C. Simsion, Graham C. Witt Developing High Quality Data Models Matthew West Location-Based Services Jochen Schiller, Agnes Voisard Managing Time in Relational Databases: How to Design, Update, and Query Temporal Data Tom Johnston, Randall Weis Database Modeling with Microsoft R Visio for Enterprise Architects Terry Halpin, Ken Evans, Patrick Hallock, Bill Maclean Designing Data-Intensive Web Applications Stephano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella Matera Mining the Web: Discovering Knowledge from Hypertext Data Soumen Chakrabarti Advanced SQL: 1999—Understanding Object-Relational and Other Advanced Features Jim Melton Database Tuning: Principles, Experiments, and Troubleshooting Techniques Dennis Shasha, Philippe Bonnet SQL: 1999—Understanding Relational Language Components Jim Melton, Alan R. Simon Information Visualization in Data Mining and Knowledge Discovery Edited by Usama Fayyad, Georges G. Grinstein, Andreas Wierse Transactional Information Systems Gerhard Weikum, Gottfried Vossen Spatial Databases Philippe Rigaux, Michel Scholl, and Agnes Voisard Managing Reference Data in Enterprise Databases Malcolm Chisholm Understanding SQL and Java Together Jim Melton, Andrew Eisenberg Database: Principles, Programming, and Performance, 2nd Edition Patrick and Elizabeth O’Neil The Object Data Standard Edited by R. G. G. Cattell, Douglas Barry Data on the Web: From Relations to Semistructured Data and XML Serge Abiteboul, Peter Buneman, Dan Suciu Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 3rd Edition Ian Witten, Eibe Frank, Mark A. Hall Joe Celko’s Data and Databases: Concepts in Practice Joe Celko Developing Time-Oriented Database Applications in SQL Richard T. Snodgrass Web Farming for the Data Warehouse Richard D. Hackathorn
    Management of Heterogeneous and Autonomous Database Systems Edited by Ahmed Elmagarmid, Marek Rusinkiewicz, Amit Sheth Object-Relational DBMSs, 2nd Edition Michael Stonebraker, Paul Brown, with Dorothy Moore Universal Database Management: A Guide to Object/Relational Technology Cynthia Maro Saracco Readings in Database Systems, 3rd Edition Edited by Michael Stonebraker, Joseph M. Hellerstein Understanding SQL’s Stored Procedures: A Complete Guide to SQL/PSM Jim Melton Principles of Multimedia Database Systems V. S. Subrahmanian Principles of Database Query Processing for Advanced Applications Clement T. Yu, Weiyi Meng Advanced Database Systems Carlo Zaniolo, Stefano Ceri, Christos Faloutsos, Richard T. Snodgrass, V. S. Subrahmanian, Roberto Zicari Principles of Transaction Processing, 2nd Edition Philip A. Bernstein, Eric Newcomer Using the New DB2: IBM’s Object-Relational Database System Don Chamberlin Distributed Algorithms Nancy A. Lynch Active Database Systems: Triggers and Rules for Advanced Database Processing Edited by Jennifer Widom, Stefano Ceri Migrating Legacy Systems: Gateways, Interfaces, and the Incremental Approach Michael L. Brodie, Michael Stonebraker Atomic Transactions Nancy Lynch, Michael Merritt, William Weihl, Alan Fekete Query Processing for Advanced Database Systems Edited by Johann Christoph Freytag, David Maier, Gottfried Vossen Transaction Processing Jim Gray, Andreas Reuter Database Transaction Models for Advanced Applications Edited by Ahmed K. Elmagarmid A Guide to Developing Client/Server SQL Applications Setrag Khoshafian, Arvola Chan, Anna Wong, Harry K. T. Wong
    Data Mining Concepts and Techniques Third Edition Jiawei Han University of Illinois at Urbana–Champaign Micheline Kamber Jian Pei Simon Fraser University AMSTERDAM • BOSTON • HEIDELBERG • LONDON SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO NEW YORK • OXFORD • PARIS • SAN DIEGO Morgan Kaufmann is an imprint of Elsevier
    Morgan Kaufmann Publishers is an imprint of Elsevier. 225 Wyman Street, Waltham, MA 02451, USA c 2012 by Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods or professional practices, may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information or methods described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data Han, Jiawei. Data mining : concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. – 3rd ed. p. cm. ISBN 978-0-12-381479-1 1. Data mining. I. Kamber, Micheline. II. Pei, Jian. III. Title. QA76.9.D343H36 2011 006.3 12–dc22 2011010635 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. For information on all Morgan Kaufmann publications, visit our Web site at www.mkp.com or www.elsevierdirect.com Printed in the United States of America 11 12 13 14 15 10 9 8 7 6 5 4 3 2 1
    To Y. Dora and Lawrence for your love and encouragement J.H. To Erik, Kevan, Kian, and Mikael for your love and inspiration M.K. To my wife, Jennifer, and daughter, Jacqueline J.P.
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