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DATA MINING
THE WEB
Uncovering Patterns in
Web Content, Structure,
and Usage
出版信息：
Copyright C 2007 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Wiley Bicentennial Logo: Richard J. Pacifico
Library of Congress Cataloging-in-Publication Data:
Markov, Zdravko, 1956–
Data-mining the Web : uncovering patterns in Web content, structure, and usage /
by Zdravko, Markov & Daniel T. Larose.
p. cm.
Includes index.
978-0-471-66655-4 (cloth)
1. Data mining. 2. Web databases. I. Larose, Daniel T. II. Title.
QA76.9.D343M38 2007
005.74 – dc22
2006025099
以下是目录：
CONTENTS
PREFACE xi
PART I
WEB STRUCTURE MINING
1 INFORMATION RETRIEVAL AND WEB SEARCH 3
Web Challenges 3
Web Search Engines 4
Topic Directories 5
Semantic Web 5
Crawling the Web 6
Web Basics 6
Web Crawlers 7
Indexing and Keyword Search 13
Document Representation 15
Implementation Considerations 19
Relevance Ranking 20
Advanced Text Search 28
Using the HTML Structure in Keyword Search 30
Evaluating Search Quality 32
Similarity Search 36
Cosine Similarity 36
Jaccard Similarity 38
Document Resemblance 41
References 43
Exercises 43
2 HYPERLINK-BASED RANKING 47
Introduction 47
Social Networks Analysis 48
PageRank 50
Authorities and Hubs 53
Link-Based Similarity Search 55
Enhanced Techniques for Page Ranking 56
References 57
Exercises 57
viiviii CONTENTS
PART II
WEB CONTENT MINING
3 CLUSTERING 61
Introduction 61
Hierarchical Agglomerative Clustering 63
k-Means Clustering 69
Probabilty-Based Clustering 73
Finite Mixture Problem 74
Classification Problem 76
Clustering Problem 78
Collaborative Filtering (Recommender Systems) 84
References 86
Exercises 86
4 EVALUATING CLUSTERING 89
Approaches to Evaluating Clustering 89
Similarity-Based Criterion Functions 90
Probabilistic Criterion Functions 95
MDL-Based Model and Feature Evaluation 100
Minimum Description Length Principle 101
MDL-Based Model Evaluation 102
Feature Selection 105
Classes-to-Clusters Evaluation 106
Precision, Recall, and F-Measure 108
Entropy 111
References 112
Exercises 112
5 CLASSIFICATION 115
General Setting and Evaluation Techniques 115
Nearest-Neighbor Algorithm 118
Feature Selection 121
Naive Bayes Algorithm 125
Numerical Approaches 131
Relational Learning 133
References 137
Exercises 138
PART III
WEB USAGE MINING
6 INTRODUCTION TO WEB USAGE MINING 143
Definition of Web Usage Mining 143
Cross-Industry Standard Process for Data Mining 144
Clickstream Analysis 147CONTENTS ix
Web Server Log Files 148
Remote Host Field 149
Date/Time Field 149
HTTP Request Field 149
Status Code Field 150
Transfer Volume (Bytes) Field 151
Common Log Format 151
Identification Field 151
Authuser Field 151
Extended Common Log Format 151
Referrer Field 152
User Agent Field 152
Example of a Web Log Record 152
Microsoft IIS Log Format 153
Auxiliary Information 154
References 154
Exercises 154
7 PREPROCESSING FOR WEB USAGE MINING 156
Need for Preprocessing the Data 156
Data Cleaning and Filtering 158
Page Extension Exploration and Filtering 161
De-Spidering the Web Log File 163
User Identification 164
Session Identification 167
Path Completion 170
Directories and the Basket Transformation 171
Further Data Preprocessing Steps 174
References 174
Exercises 174
8 EXPLORATORY DATA ANALYSIS FOR WEB USAGE MINING 177
Introduction 177
Number of Visit Actions 177
Session Duration 178
Relationship between Visit Actions and Session Duration 181
Average Time per Page 183
Duration for Individual Pages 185
References 188
Exercises 188
9 MODELING FOR WEB USAGE MINING: CLUSTERING,
ASSOCIATION, AND CLASSIFICATION 191
Introduction 191
Modeling Methodology 192
Definition of Clustering 193
The BIRCH Clustering Algorithm 194
Affinity Analysis and the A Priori Algorithm 197x CONTENTS
Discretizing the Numerical Variables: Binning 199
Applying the A Priori Algorithm to the CCSU Web Log Data 201
Classification and Regression Trees 204
The C4.5 Algorithm 208
References 210
Exercises 211
INDEX 213
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