Data-Mining the Web

DATA MINING THE WEB......Page 3
 CONTENTS......Page 9
 PREFACE......Page 13
  ACKNOWLEDGMENTS......Page 17
 PART I WEB STRUCTURE MINING......Page 19
   Web Challenges......Page 21
    Web Search Engines......Page 22
    Semantic Web......Page 23
    Web Basics......Page 24
    Web Crawlers......Page 25
   Indexing and Keyword Search......Page 31
    Document Representation......Page 33
    Implementation Considerations......Page 37
    Relevance Ranking......Page 38
    Advanced Text Search......Page 46
    Using the HTML Structure in Keyword Search......Page 48
   Evaluating Search Quality......Page 50
    Cosine Similarity......Page 54
    Jaccard Similarity......Page 56
    Document Resemblance......Page 59
   Exercises......Page 61
   Introduction......Page 65
   Social Networks Analysis......Page 66
   PageRank......Page 68
   Authorities and Hubs......Page 71
   Link-Based Similarity Search......Page 73
   Enhanced Techniques for Page Ranking......Page 74
   Exercises......Page 75
 PART II WEB CONTENT MINING......Page 77
   Introduction......Page 79
    Hierarchical Agglomerative Clustering......Page 81
   k-Means Clustering......Page 87
   Probabilty-Based Clustering......Page 91
    Finite Mixture Problem......Page 92
    Classification Problem......Page 94
    Clustering Problem......Page 96
   Collaborative Filtering (Recommender Systems)......Page 102
   Exercises......Page 104
   Approaches to Evaluating Clustering......Page 107
   Similarity-Based Criterion Functions......Page 108
   Probabilistic Criterion Functions......Page 113
   MDL-Based Model and Feature Evaluation......Page 118
    Minimum Description Length Principle......Page 119
    MDL-Based Model Evaluation......Page 120
    Feature Selection......Page 123
   Classes-to-Clusters Evaluation......Page 124
   Precision, Recall, and F-Measure......Page 126
   Entropy......Page 129
   Exercises......Page 130
   General Setting and Evaluation Techniques......Page 133
   Nearest-Neighbor Algorithm......Page 136
   Feature Selection......Page 139
   Naive Bayes Algorithm......Page 143
   Numerical Approaches......Page 149
   Relational Learning......Page 151
   References......Page 155
   Exercises......Page 156
 PART III WEB USAGE MINING......Page 159
   Definition of Web Usage Mining......Page 161
   Cross-Industry Standard Process for Data Mining......Page 162
   Clickstream Analysis......Page 165
   Web Server Log Files......Page 166
    HTTP Request Field......Page 167
    Status Code Field......Page 168
   Extended Common Log Format......Page 169
    Example of a Web Log Record......Page 170
   Microsoft IIS Log Format......Page 171
   Exercises......Page 172
   Need for Preprocessing the Data......Page 174
   Data Cleaning and Filtering......Page 176
    Page Extension Exploration and Filtering......Page 179
   De-Spidering the Web Log File......Page 181
   User Identification......Page 182
   Session Identification......Page 186
   Path Completion......Page 188
   Directories and the Basket Transformation......Page 189
   Exercises......Page 192
   Number of Visit Actions......Page 195
   Session Duration......Page 196
   Relationship between Visit Actions and Session Duration......Page 199
   Average Time per Page......Page 201
   Duration for Individual Pages......Page 203
   Exercises......Page 206
   Introduction......Page 209
   Modeling Methodology......Page 210
   Definition of Clustering......Page 211
   The BIRCH Clustering Algorithm......Page 212
   Affinity Analysis and the A Priori Algorithm......Page 215
   Discretizing the Numerical Variables: Binning......Page 217
   Applying the A Priori Algorithm to the CCSU Web Log Data......Page 219
   Classification and Regression Trees......Page 222
   The C4.5 Algorithm......Page 226
   References......Page 228
   Exercises......Page 229
 INDEX......Page 231