Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.

Transcription

1 Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How to Read this Book p. 11 Bibliographic Notes p. 12 Data Mining Foundations Association Rules and Sequential Patterns p. 13 Basic Concepts of Association Rules p. 13 Apriori Algorithm p. 16 Frequent Itemset Generation p. 16 Association Rule Generation p. 20 Data Formats for Association Rule Mining p. 22 Mining with Multiple Minimum Supports p. 22 Extended Model p. 24 Mining Algorithm p. 26 Rule Generation p. 31 Mining Class Association Rules p. 32 Problem Definition p. 32 Mining Algorithm p. 34 Mining with Multiple Minimum Supports p. 37 Basic Concepts of Sequential Patterns p. 37 Mining Sequential Patterns Based on GSP p. 39 GSP Algorithm p. 39 Mining with Multiple Minimum Supports p. 41 Mining Sequential Patterns Based on PrefixSpan p. 45 PrefixSpan Algorithm p. 46 Mining with Multiple Minimum Supports p. 48 Generating Rules from Sequential Patterns p. 49 Sequential Rules p. 50 Label Sequential Rules p. 50 Class Sequential Rules p. 51 Bibliographic Notes p. 52 Supervised Learning p. 55 Basic Concepts p. 55 Decision Tree Induction p. 59 Learning Algorithm p. 62 Impurity Function p. 63

2 Handling of Continuous Attributes p. 67 Some Other Issues p. 68 Classifier Evaluation p. 71 Evaluation Methods p. 71 Precision, Recall, F-score and Breakeven Point p. 73 Rule Induction p. 75 Sequential Covering p. 75 Rule Learning: Learn-One-Rule Function p. 78 Discussion p. 81 Classification Based on Associations p. 81 Classification Using Class Association Rules p. 82 Class-Association Rules as Features p. 86 Classification Using Normal Association Rules p. 86 Naive Bayesian Classification p. 87 Naive Bayesian Text Classification p. 91 Probabilistic Framework p. 92 Naive Bayesian Model p. 93 Discussion p. 96 Support Vector Machines p. 97 Linear SVM: Separable Case p. 99 Linear SVM: Non-Separable Case p. 105 Nonlinear SVM: Kernel Functions p. 108 K-Nearest Neighbor Learning p. 112 Ensemble of Classifiers p. 113 Bagging p. 114 Boosting p. 114 Bibliographic Notes p. 115 Unsupervised Learning p. 117 Basic Concepts p. 117 K-means Clustering p. 120 K-means Algorithm p. 120 Disk Version of the K-means Algorithm p. 123 Strengths and Weaknesses p. 124 Representation of Clusters p. 128 Common Ways of Representing Clusters p. 129 Clusters of Arbitrary Shapes p. 130 Hierarchical Clustering p. 131 Single-Link Method p. 133 Complete-Link Method p. 133 Average-Link Method p. 134 Strengths and Weaknesses p. 134

3 Distance Functions p. 135 Numeric Attributes p. 135 Binary and Nominal Attributes p. 136 Text Documents p. 138 Data Standardization p. 139 Handling of Mixed Attributes p. 141 Which Clustering Algorithm to Use? p. 143 Cluster Evaluation p. 143 Discovering Holes and Data Regions p. 146 Bibliographic Notes p. 149 Partially Supervised Learning p. 151 Learning from Labeled and Unlabeled Examples p. 151 EM Algorithm with Naive Bayesian Classification p. 153 Co-Training p. 156 Self-Training p. 158 Transductive Support Vector Machines p. 159 Graph-Based Methods p. 160 Discussion p. 164 Learning from Positive and Unlabeled Examples p. 165 Applications of PU Learning p. 165 Theoretical Foundation p. 168 Building Classifiers: Two-Step Approach p. 169 Building Classifiers: Direct Approach p. 175 Discussion p. 178 Derivation of EM for Naive Bayesian Classification p. 179 Bibliographic Notes p. 181 Web Mining Information Retrieval and Web Search p. 183 Basic Concepts of Information Retrieval p. 184 Information Retrieval Models p. 187 Boolean Model p. 188 Vector Space Model p. 188 Statistical Language Model p. 191 Relevance Feedback p. 192 Evaluation Measures p. 195 Text and Web Page Pre-Processing p. 199 Stopword Removal p. 199 Stemming p. 200 Other Pre-Processing Tasks for Text p. 200 Web Page Pre-Processing p. 201 Duplicate Detection p. 203

4 Inverted Index and Its Compression p. 204 Inverted Index p. 204 Search Using an Inverted Index p. 206 Index Construction p. 207 Index Compression p. 209 Latent Semantic Indexing p. 215 Singular Value Decomposition p. 215 Query and Retrieval p. 218 An Example p. 219 Discussion p. 221 Web Search p. 222 Meta-Search: Combining Multiple Rankings p. 225 Combination Using Similarity Scores p. 226 Combination Using Rank Positions p. 227 Web Spamming p. 229 Content Spamming p. 230 Link Spamming p. 231 Hiding Techniques p. 233 Combating Spam p. 234 Bibliographic Notes p. 235 Link Analysis p. 237 Social Network Analysis p. 238 Centrality p. 238 Prestige p. 241 Co-Citation and Bibliographic Coupling p. 243 Co-Citation p. 244 Bibliographic Coupling p. 245 PageRank p. 245 PageRank Algorithm p. 246 Strengths and Weaknesses of PageRank p. 253 Timed PageRank p. 254 Hits p. 255 Hits Algorithm p. 256 Finding Other Eigenvectors p. 259 Relationships with Co-Citation and Bibliographic Coupling p. 259 Strengths and Weaknesses of Hits p. 260 Community Discovery p. 261 Problem Definition p. 262 Bipartite Core Communities p. 264 Maximum Flow Communities p Communities Based on Betweenness p. 268

5 Overlapping Communities of Named Entities p. 270 Bibliographic Notes p. 271 Web Crawling p. 273 A Basic Crawler Algorithm p. 274 Breadth-First Crawlers p. 275 Preferential Crawlers p. 276 Implementation Issues p. 277 Fetching p. 277 Parsing p. 278 Stopword Removal and Stemming p. 280 Link Extraction and Canonicalization p. 280 Spider Traps p. 282 Page Repository p. 283 Concurrency p. 284 Universal Crawlers p. 285 Scalability p. 286 Coverage vs Freshness vs Importance p. 288 Focused Crawlers p. 289 Topical Crawlers p. 292 Topical Locality and Cues p. 294 Best-First Variations p. 300 Adaptation p. 303 Evaluation p. 310 Crawler Ethics and Conflicts p. 315 Some New Developments p. 318 Bibliographic Notes p. 320 Structured Data Extraction: Wrapper Generation p. 323 Preliminaries p. 324 Two Types of Data Rich Pages p. 324 Data Model p. 326 HTML Mark-Up Encoding of Data Instances p. 328 Wrapper Induction p. 330 Extraction from a Page p. 330 Learning Extraction Rules p. 333 Identifying Informative Examples p. 337 Wrapper Maintenance p. 338 Instance-Based Wrapper Learning p. 338 Automatic Wrapper Generation: Problems p. 341 Two Extraction Problems p. 342 Patterns as Regular Expressions p. 343 String Matching and Tree Matching p. 344

6 String Edit Distance p. 344 Tree Matching p. 346 Multiple Alignment p. 350 Center Star Method p. 350 Partial Tree Alignment p. 351 Building DOM Trees p. 356 Extraction Based on a Single List Page: Flat Data Records p. 357 Two Observations about Data Records p. 358 Mining Data Regions p. 359 Identifying Data Records in Data Regions p. 364 Data Item Alignment and Extraction p. 365 Making Use of Visual Information p. 366 Some Other Techniques p. 366 Extraction Based on a Single List Page: Nested Data Records p. 367 Extraction Based on Multiple Pages p. 373 Using Techniques in Previous Sections p. 373 RoadRunner Algorithm p. 374 Some Other Issues p. 375 Extraction from Other Pages p. 375 Disjunction or Optional p. 376 A Set Type or a Tuple Type p. 377 Labeling and Integration p. 378 Domain Specific Extraction p. 378 Discussion p. 379 Bibliographic Notes p. 379 Information Integration p. 381 Introduction to Schema Matching p. 382 Pre-Processing for Schema Matching p. 384 Schema-Level Match p. 385 Linguistic Approaches p. 385 Constraint Based Approaches p. 386 Domain and Instance-Level Matching p. 387 Combining Similarities p :m Match p. 391 Some Other Issues p. 392 Reuse of Previous Match Results p. 392 Matching a Large Number of Schemas p. 393 Schema Match Results p. 393 User Interactions p. 394 Integration of Web Query Interfaces p. 394 A Clustering Based Approach p. 397

7 A Correlation Based Approach p. 400 An Instance Based Approach p. 403 Constructing a Unified Global Query Interface p. 406 Structural Appropriateness and the Merge Algorithm p. 406 Lexical Appropriateness p. 408 Instance Appropriateness p. 409 Bibliographic Notes p. 410 Opinion Mining p. 411 Sentiment Classification p. 412 Classification Based on Sentiment Phrases p. 413 Classification Using Text Classification Methods p. 415 Classification Using a Score Function p. 416 Feature-Based Opinion Mining and Summarization p. 417 Problem Definition p. 418 Object Feature Extraction p. 424 Feature Extraction from Pros and Cons of Format 1 p. 425 Feature Extraction from Reviews of of Formats 2 and 3 p. 429 Opinion Orientation Classification p. 430 Comparative Sentence and Relation Mining p. 432 Problem Definition p. 433 Identification of Gradable Comparative Sentences p. 435 Extraction of Comparative Relations p. 437 Opinion Search p. 439 Opinion Spam p. 441 Objectives and Actions of Opinion Spamming p. 441 Types of Spam and Spammers p. 442 Hiding Techniques p. 443 Spam Detection p. 444 Bibliographic Notes p. 446 Web Usage Mining p. 449 Data Collection and Pre-Processing p. 450 Sources and Types of Data p. 452 Key Elements of Web Usage Data Pre-Processing p. 455 Data Modeling for Web Usage Mining p. 462 Discovery and Analysis of Web Usage Patterns p. 466 Session and Visitor Analysis p. 466 Cluster Analysis and Visitor Segmentation p. 467 Association and Correlation Analysis p. 471 Analysis of Sequential and Navigational Patterns p. 475 Classification and Prediction Based on Web User Transactions p. 479 Discussion and Outlook p. 482

8 Bibliographic Notes p. 482 References p. 485 Index p. 517 Table of Contents provided by Blackwell's Book Services and R.R. Bowker. Used with permission.