Table of Contents - Lagout.org

1 Table of Contents Cover Image Front Matter Copyright Dedication Foreword Foreword to Second Edition Preface Acknowledgments About the Authors 1. Introduction Why Data Mining? What Is Data Mining? What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies Are Used? Which Kinds of Applications Are Targeted? Major Issues in Data Mining Summary Exercises Bibliographic Notes 2. Getting to Know Your Data Data Objects and Attribute Types Basic Statistical Descriptions of Data Data Visualization Measuring Data Similarity and Dissimilarity Summary Exercises Bibliographic Notes 3. Data Preprocessing Data Preprocessing: An Overview Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary Exercises Bibliographic Notes 4. Data Warehousing and Online Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Data Warehouse Implementation Data Generalization by Attribute-Oriented Induction Summary Exercises 5.

2 Data Cube Technology Data Cube Computation: Preliminary Concepts Data Cube Computation Methods Processing Advanced Kinds of Queries by Exploring Cube Technology Multidimensional Data Analysis in Cube Space Summary Exercises Bibliographic Notes 6. Mining Frequent Patterns, Associations, and Correlations Basic Concepts Frequent Itemset Mining Methods Which Patterns Are Interesting? Pattern Evaluation Methods Summary Exercises Bibliographic Notes 7. Advanced Pattern Mining Pattern Mining: A Road Map Pattern Mining in Multilevel, Multidimensional Space Constraint-Based Frequent Pattern Mining Mining High-Dimensional Data and Colossal Patterns Mining Compressed or Approximate Patterns Pattern Exploration and Application Summary Exercises Bibliographic Notes 8. classification Basic Concepts Decision Tree Induction Bayes classification Methods Rule-Based classification Model Evaluation and Selection Techniques to Improve classification Accuracy Summary Exercises Bibliographic Notes 9.

3 classification Bayesian Belief Networks classification by Backpropagation Support Vector Machines classification using Frequent Patterns Lazy Learners (or Learning from Your Neighbors) Other classification Methods Additional Topics Regarding classification Exercises Bibliographic Notes 10. Cluster Analysis Cluster Analysis Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Evaluation of Clustering Summary Exercises Bibliographic Notes 11. Advanced Cluster Analysis Probabilistic Model-Based Clustering Clustering High-Dimensional Data Clustering Graph and Network Data Clustering with Constraints Exercises Bibliographic Notes 12. Outlier detection Outliers and Outlier Analysis Outlier detection Methods Statistical Approaches Proximity-Based Approaches Clustering-Based Approaches classification -Based Approaches Mining Contextual and Collective Outliers Outlier detection in High-Dimensional Data Summary Exercises Bibliographic Notes 13.

4 Data Mining Trends and Research Frontiers Mining Complex Data Types Other Methodologies of Data Mining Data Mining Data Mining and Society Data Mining Trends Summary Exercises Bibliographic Notes Bibliography Index Front MatterData MiningThird EditionThe Morgan Kaufmann Series in Data Management Systems (Selected Titles)Joe Celko's Data, Measurements, and Standards in SQLJoe CelkoInformation Modeling and Relational Databases, 2nd EditionTerry Halpin, Tony MorganJoe Celko's Thinking in SetsJoe CelkoBusiness MetadataBill Inmon, Bonnie O'Neil, Lowell FrymanUnleashing Web Vossen, Stephan HagemannEnterprise Knowledge ManagementDavid LoshinThe Practitioner's Guide to Data Quality ImprovementDavid LoshinBusiness Process Change, 2nd EditionPaul HarmonIT Manager's Handbook, 2nd EditionBill Holtsnider, Brian JaffeJoe Celko's Puzzles and Answers, 2nd EditionJoe CelkoArchitecture and Patterns for IT Service Management, 2nd Edition, Resource Planning and GovernanceCharles BetzJoe Celko's Analytics and OLAP in SQLJoe CelkoData Preparation for Data Mining using SASM amdouh RefaatQuerying XML: XQuery, XPath, and SQL/ XML in ContextJim Melton, Stephen BuxtonData Mining.

5 Concepts and Techniques, 3rd EditionJiawei Han, Micheline Kamber, Jian PeiDatabase Modeling and Design: Logical Design, 5th Edition Toby J. Teorey, Sam S. Lightstone, Thomas P. Nadeau, H. V. JagadishFoundations of Multidimensional and Metric Data StructuresHanan SametJoe Celko's SQL for Smarties: Advanced SQL Programming, 4th EditionJoe CelkoMoving Objects DatabasesRalf Hartmut G ting, Markus SchneiderJoe Celko's SQL Programming StyleJoe CelkoFuzzy Modeling and genetic Algorithms for Data Mining and ExplorationEarl CoxData Modeling Essentials, 3rd EditionGraeme C. Simsion, Graham C. WittDeveloping High Quality Data ModelsMatthew WestLocation-Based ServicesJochen Schiller, Agnes VoisardManaging Time in Relational Databases: How to Design, Update, and Query Temporal DataTom Johnston, Randall WeisDatabase Modeling with Microsoft Visio for Enterprise ArchitectsTerry Halpin, Ken Evans, Patrick Hallock, Bill MacleanDesigning Data-Intensive Web ApplicationsStephano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella MateraMining the Web: Discovering Knowledge from Hypertext DataSoumen ChakrabartiAdvanced SQL: 1999 Understanding Object-Relational and Other Advanced FeaturesJim MeltonDatabase Tuning: Principles, Experiments, and Troubleshooting TechniquesDennis Shasha, Philippe BonnetSQL: 1999 Understanding Relational Language ComponentsJim Melton, Alan R.

6 SimonInformation Visualization in Data Mining and KnowledgeDiscoveryEdited by Usama Fayyad, Georges G. Grinstein, Andreas WierseTransactional Information SystemsGerhard Weikum, Gottfried VossenSpatial Databases Philippe Rigaux, Michel Scholl, and Agnes VoisardManaging Reference Data in Enterprise DatabasesMalcolm ChisholmUnderstanding SQL and Java TogetherJim Melton, Andrew EisenbergDatabase: Principles, Programming, and Performance, 2nd EditionPatrick and Elizabeth O'NeilThe Object Data StandardEdited by R. G. G. Cattell, Douglas BarryData on the Web: From Relations to Semistructured Data and XMLS erge Abiteboul, Peter Buneman, Dan SuciuData Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 3rd EditionIan Witten, Eibe Frank, Mark A. HallJoe Celko's Data and Databases: Concepts in PracticeJoe CelkoDeveloping Time-Oriented Database Applications in SQLR ichard T. SnodgrassWeb Farming for the Data WarehouseRichard D.

7 HackathornManagement of Heterogeneous and Autonomous Database SystemsEdited by Ahmed Elmagarmid, Marek Rusinkiewicz, Amit ShethObject-Relational DBMSs, 2nd EditionMichael Stonebraker, Paul Brown, with Dorothy MooreUniversal Database Management: A Guide to Object/Relational TechnologyCynthia Maro SaraccoReadings in Database Systems, 3rd EditionEdited by Michael Stonebraker, Joseph M. HellersteinUnderstanding SQL's Stored Procedures: A Complete Guide to SQL/PSMJim MeltonPrinciples of Multimedia Database SystemsV. S. SubrahmanianPrinciples of Database Query Processing for Advanced ApplicationsClement T. Yu, Weiyi MengAdvanced Database SystemsCarlo Zaniolo, Stefano Ceri, Christos Faloutsos, Richard T. Snodgrass, V. S. Subrahmanian, Roberto ZicariPrinciples of Transaction Processing, 2nd Edition Philip A. Bernstein, Eric NewcomerUsing the New DB2: IBM's Object-Relational Database SystemDon ChamberlinDistributed AlgorithmsNancy A.

8 LynchActive Database Systems: Triggers and Rules for Advanced Database ProcessingEdited by Jennifer Widom, Stefano CeriMigrating Legacy Systems: Gateways, Interfaces, and the Incremental ApproachMichael L. Brodie, Michael StonebrakerAtomic TransactionsNancy Lynch, Michael Merritt, William Weihl, Alan FeketeQuery Processing for Advanced Database SystemsEdited by Johann Christoph Freytag, David Maier, Gottfried VossenTransaction ProcessingJim Gray, Andreas ReuterDatabase Transaction Models for Advanced ApplicationsEdited by Ahmed K. ElmagarmidA Guide to Developing Client/Server SQL ApplicationsSetrag Khoshafian, Arvola Chan, Anna Wong, Harry K. T. WongData MiningConcepts and TechniquesThird EditionJiawei HanUniversity of Illinois at Urbana ChampaignMicheline KamberJian PeiSimon Fraser University AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Morgan Kaufmann is an imprint of Elsevier CopyrightMorgan Kaufmann Publishers is an imprint of Wyman Street, Waltham, MA 02451, USA 2012 by Elsevier Inc.

9 All rights 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: book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). NoticesKnowledge 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.

10 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 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 of Congress Cataloging-in-Publication DataHan, mining : concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. 3rd 978-0-12-381479-11. Data mining. I. Kamber, Micheline. II. Pei, Jian.