Verfügbarkeit: Data-mining the web

Data-mining the web: uncovering patterns in Web content, structure, and usage

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Bibliographische Detailangaben
Hauptverfasser:	Markov, Zdravko (VerfasserIn), Larose, Daniel T. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Hoboken, NJ Wiley 2007
Schriftenreihe:	Wiley series on methods and applications in data mining
Schlagworte:	Bases de données sur le Web Exploration de données (Informatique) Data mining Web databases Data Mining
Online-Zugang:	Beschreibung für Leser Table of contents only Inhaltsverzeichnis
Beschreibung:	XVI, 218 S. Ill., graph. Darst.
ISBN:	0471666556 9780471666554

Internformat

MARC


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Datensatz im Suchindex

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adam_text	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 vii Viii CONTENTS PART II WEB CONTENT MINING 3 CLUSTERING 61 Introduction 61 Hierarchical Agglomerative Clustering 63 A 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 l oo Minimum Description Length Principle 1 ()1 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 147 CONTENTS 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 18a Exercises 188 9 MODELING FOR WEB USAGE MINING: CLUSTERING, ASSOCIATION, AND CLASSIFICATION 191 Introduction 19 Modeling Methodology 192 Definition of Clustering 193 The BIRCH Clustering Algorithm 194 Affinity Analysis and the A Priori Algorithm 197 X 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
adam_txt	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 vii Viii CONTENTS PART II WEB CONTENT MINING 3 CLUSTERING 61 Introduction 61 Hierarchical Agglomerative Clustering 63 A 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 l oo Minimum Description Length Principle 1 ()1 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 147 CONTENTS 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 18a Exercises 188 9 MODELING FOR WEB USAGE MINING: CLUSTERING, ASSOCIATION, AND CLASSIFICATION 191 Introduction 19' Modeling Methodology 192 Definition of Clustering 193 The BIRCH Clustering Algorithm 194 Affinity Analysis and the A Priori Algorithm 197 X 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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spellingShingle	Markov, Zdravko Larose, Daniel T. Data-mining the web uncovering patterns in Web content, structure, and usage Bases de données sur le Web Exploration de données (Informatique) Data mining Web databases Data Mining (DE-588)4428654-5 gnd
subject_GND	(DE-588)4428654-5
title	Data-mining the web uncovering patterns in Web content, structure, and usage
title_alt	Data mining the web
title_auth	Data-mining the web uncovering patterns in Web content, structure, and usage
title_exact_search	Data-mining the web uncovering patterns in Web content, structure, and usage
title_exact_search_txtP	Data-mining the web uncovering patterns in Web content, structure, and usage
title_full	Data-mining the web uncovering patterns in Web content, structure, and usage Zdravko Markov and Daniel T. Larose
title_fullStr	Data-mining the web uncovering patterns in Web content, structure, and usage Zdravko Markov and Daniel T. Larose
title_full_unstemmed	Data-mining the web uncovering patterns in Web content, structure, and usage Zdravko Markov and Daniel T. Larose
title_short	Data-mining the web
title_sort	data mining the web uncovering patterns in web content structure and usage
title_sub	uncovering patterns in Web content, structure, and usage
topic	Bases de données sur le Web Exploration de données (Informatique) Data mining Web databases Data Mining (DE-588)4428654-5 gnd
topic_facet	Bases de données sur le Web Exploration de données (Informatique) Data mining Web databases Data Mining
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