Chakrabarti / Güting / Cox Data Mining: Know It All

1. Auflage 2008
ISBN: 978-0-08-087788-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 480 Seiten

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology.
The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining.
This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader's technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Soumen Chakrabarti is assistant Professor in Computer Science and Engineering at the Indian Institute of Technology, Bombay. Prior to joining IIT, he worked on hypertext databases and data mining at IBM Almaden Research Center. He has developed three systems and holds five patents in this area. Chakrabarti has served as a vice-chair and program committee member for many conferences, including WWW, SIGIR, ICDE, and KDD, and as a guest editor of the IEEE TKDE special issue on mining and searching the Web. His work on focused crawling received the Best Paper award at the 8th International World Wide Web Conference (1999). He holds a Ph.D. from the University of California, Berkeley.

Autoren/Hrsg.

Chakrabarti, Soumen

Güting, Ralf Hartmut

Cox, Earl

Han, Jiawei

Frank, Eibe

Jiang, Xia

Kamber, Micheline

Lightstone, Sam S.

Nadeau, Thomas P.

Kamb, Micheline

Neapolitan, Richard E.

Pyle, Dorian

Refaat, Mamdouh

Schneider, Markus

Teorey, Toby J.

Witten, Ian H.

Inhaltsverzeichnis

1;Front cover;1
2;Data Mining: Know It All;4
3;Copyright page;5
4;Table of contents;6
5;About This Book;10
6;Contributing Authors;12
7;Chapter 1 What’s It All About?;18
7.1;1.1DATA MINING AND MACHINE LEARNING;18
7.2;1.2SIMPLE EXAMPLES: THE WEATHER PROBLEM AND OTHERS;24
7.3;1.3FIELDED APPLICATIONS;37
7.4;1.4MACHINE LEARNING AND STATISTICs;44
7.5;1.5GENERALIZATION AS SEARCH;45
7.6;1.6DATA MINING AND ETHICS;49
7.7;1.7RESOURCES;51
8;Chapter 2 Data Acquisition and Integration;54
8.1;2.1INTRODUCTION;54
8.2;2.2SOURCES OF DATA;54
8.3;2.3VARIABLE TYPES;56
8.4;2.4DATA ROLLUP;58
8.5;2.5ROLLUP WITH SUMS, AVERAGES, AND COUNTS;65
8.6;2.6CALCULATION OF THE MODE;66
8.7;2.7DATA INTEGRATION;67
9;Chapter 3 Data Preprocessing;74
9.1;3.1WHY PREPROCESS THE DATA?;75
9.2;3.2DESCRIPTIVE DATA SUMMARIZATION;78
9.3;3.3DATA CLEANING;89
9.4;3.4DATA INTEGRATION AND TRANSFORMATION;95
9.5;3.5DATA REDUCTION;101
9.6;3.6DATA DISCRETIZATION AND CONCEPT HIERARCHY GENERATION;115
9.7;3.7SUMMARY;125
9.8;3.8RESOURCES;126
10;Chapter 4 Physical Design for Decision Support, Warehousing, and OLAP;130
10.1;4.1WHAT IS ONLINE ANALYTICAL PROCESSING?;130
10.2;4.2DIMENSION HIERARCHIES;133
10.3;4.3STAR AND SNOWFLAKE SCHEMAS;134
10.4;4.4WAREHOUSES AND MARTS;136
10.5;4.5SCALING UP THE SYSTEM;139
10.6;4.6DSS, WAREHOUSING, AND OLAP DESIGN CONSIDERATIONS;141
10.7;4.7USAGE SYNTAX AND EXAMPLES FOR MAJOR DATABASE SERVERS;142
10.8;4.8SUMMARY;145
10.9;4.9LITERATURE SUMMARY;146
10.10;RESOURCES;146
11;Chapter 5 Algorithms: The Basic Methods;148
11.1;5.1INFERRING RUDIMENTARY RULES;149
11.2;5.2STATISTICAL MODELING;153
11.3;5.3DIVIDE AND CONQUER: CONSTRUCTING DECISION TREES;161
11.4;5.4COVERING ALGORITHMS: CONSTRUCTING RULES;170
11.5;5.5MINING ASSOCIATION RULES;177
11.6;5.6LINEAR MODELS;185
11.7;5.7INSTANCE-BASED LEARNING;193
11.8;5.8CLUSTERING;201
11.9;5.9RESOURCES;205
12;Chapter 6 Further Techniques in Decision Analysis;208
12.1;6.1MODELING RISK PREFERENCES;208
12.2;6.2ANALYZING RISK DIRECTLY;215
12.3;6.3DOMINANCE;217
12.4;6.4SENSITIVITY ANALYSIS;222
12.5;6.5VALUE OF INFORMATION;232
12.6;6.6NORMATIVE DECISION ANALYSIS;237
13;Chapter 7 Fundamental Concepts of Genetic Algorithms;238
13.1;7.1THE VOCABULARY OF GENETIC ALGORITHMS;239
13.2;7.2OVERVIEW;247
13.3;7.3THE ARCHITECTURE OF A GENETIC ALGORITHM;258
13.4;7.4PRACTICAL ISSUES IN USING A GENETIC ALGORITHM;302
13.5;7.5REVIEW;307
13.6;7.6RESOURCES;307
14;Chapter 8 Data Structures and Algorithms for Moving Objects Types;310
14.1;8.1DATA STRUCTURES;310
14.2;8.2ALGORITHMS FOR OPERATIONS ON TEMPORAL DATA TYPES;315
14.3;8.3ALGORITHMS FOR LIFTED OPERATIONS;327
14.4;8.4RESOURCES;336
15;Chapter 9 Improving the Model;338
15.1;9.1LEARNING FROM ERRORS;340
15.2;9.2IMPROVING MODEL QUALITY, SOLVING PROBLEMS;360
15.3;9.3SUMMARY;412
16;Chapter 10 Social Network Analysis;414
16.1;10.1SOCIAL SCIENCES AND BIBLIOMETRY;415
16.2;10.2PAGERANK AND HYPERLINK-INDUCED TOPIC SEARCH;417
16.3;10.3SHORTCOMINGS OF THE COARSE-GRAINED GRAPH MODEL;427
16.4;10.4ENHANCED MODELS AND TECHNIQUES;433
16.5;10.5EVALUATION OF TOPIC DISTILLATION;441
16.6;10.6MEASURING AND MODELING THE WEB;447
16.7;10.7RESOURCES;457
17;Index;460