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BK

Berlin : Springer, c2007

xix, 532 s. : il. ; 24 cm

ISBN 978-3-540-37881-5 (váz.) ISBN !3-540-37881-2 (chyb.)

Obsahuje tabulky

Bibliografie na s. [485]-515, rejstřík

000100432

Table of Contents // 1. Introduction ... 1 // 1.1. What is the World Wide Web? ...1 // 1.2. A Brief History of the Web and the Internet ...2 // 1.3. Web Data Mining ...4 // 1.3.1. What is Data Mining?...6 // 1.3.2. What is Web Mining? ...6 // 1.4. Summary of Chapters ...8 // 1.5. How to Read this Book...11 // Bibliographic Notes...12 // Part I: Data Mining Foundations // 2. Association Rules and Sequential Patterns ...13 // 2.1. Basic Concepts of Association Rules...13 // 2.2. Apriori Algorithm ...16 // 2.2.1 . Frequent Itemset Generation ...16 // 2.2.2 Association Rule Generation ...20 // 2.3. Data Formats for Association Rule Mining ...22 // 2.4. Mining with Multiple Minimum Supports...22 // 2.4.1 Extended Model ...24 // 2.4.2. Mining Algorithm ...26 // 2.4.3. Rule Generation...31 // 2.5. Mining Class Association Rules ...32 // 2.5.1. Problem Definition...32 // 2.5.2. Mining Algorithm ...34 // 2.5.3. Mining with Multiple Minimum Supports ...37 // XII Table of Contents // 2.6. Basic Concepts of Sequential Patterns ...37 // 2.7. Mining Sequential Patterns Based on GSP...39 // 2.7.1. GSP Algorithm ...39 // 2.7.2. Mining with Multiple Minimum Supports ...41 // 2.8. Mining Sequential Patterns Based on PrefixSpan...45 // 2.8.1. PrefixSpan Algorithm ...46 // 2.8.2. Mining with Multiple Minimum Supports ...48 // 2.9. Generating Rules from Sequential Patterns...49 // 2.9.1. Sequential Rules...50 // 2.9.2. Label Sequential Rules...50 // 2.9.3. Class Sequential Rules...51 // Bibliographic

Notes...52 // 3. Supervised Learning...55 // 3.1. Basic Concepts...55 // 3.2. Decision Tree Induction ...59 // 3.2.1. Learning Algorithm...62 // 3.2.2. Impurity Function ...63 // 3.2.3. Handling of Continuous Attributes ...67 // 3.2.4. Some Other Issues ...68 // 3.3. Classifier Evaluation ...71 // 3.3.1. Evaluation Methods ...71 // 3.3.2. Precision, Recall, F-score and Breakeven Point ..73 // 3.4. Rule Induction...75 // 3.4.1. Sequential Covering...75 // 3.4.2. Rule Learning: Learn-One-Rule Function...78 // 3.4.3. Discussion...81 // 3.5. Classification Based on Associations ...81 // 3.5.1. Classification Using Class Association Rules...82 // 3.5.2. Class Association Rules as Features ...86 // 3.5.3. Classification Using Normal Association Rules ...86 // 3.6. Naive Bayesian Classification...87 // 3.7. Naive Bayesian Text Classification ...91 // 3.7.1. Probabilistic Framework...92 // 3.7.2. Naive Bayesian Model ...93 // 3.7.3. Discussion...96 // 3.8. Support Vector Machines...97 // 3.8.1. Linear SVM: Separable Case ...99 // Table of Contents // XIII // 3.8.2. Linear SVM: Non-Separable Case... 105 // 3.8.3. Nonlinear SVM: Kernel Functions...108 // 3.9. K-Nearest Neighbor Learning...112 // 3.10. Ensemble of Classifiers...113 // 3.10.1. Bagging ...114 // 3.10.2. Boosting ...114 // Bibliographic Notes...115 // 4. Unsupervised Learning ...117 // 4.1. Basic Concepts ... 117 // 4.2. K-means Clustering ...120 // 4.2.1. K-means Algorithm ...120 // 4.2.2. Disk Version of the K-means Algorithm

...123 // 4.2.3. Strengths and Weaknesses...124 // 4.3. Representation of Clusters ...128 // 4.3.1. Common Ways of Representing Clusters...129 // 4.3.2. Clusters of Arbitrary Shapes ...130 // 4.4. Hierarchical Clustering ...131 // 4.4.1. Single-Link Method ... 133 // 4.4.2. Complete-Link Method ...133 // 4.4.3. Average-Link Method ...134 // 4.4.4. Strengths and Weaknesses...134 // 4.5. Distance Functions ...135 // 4.5.1. Numeric Attributes ... 135 // 4.5.2. Binary and Nominal Attributes ...136 // 4.5.3. Text Documents ... 138 // 4.6. Data Standardization ...139 // 4.7. Handling of Mixed Attributes ...141 // 4.8. Which Clustering Algorithm to Use? ...143 // 4.9. Cluster Evaluation...143 // 4.10. Discovering Holes and Data Regions ...146 // Bibliographic Notes...149 // 5. Partially Supervised Learning... 151 // 5.1. Learning from Labeled and Unlabeled Examples ... 151 // 5.1.1. EM Algorithm with Naive Bayesian Classification • 153 // XIV Table of Contents // 5.1.2. Co-Training ...156 // 5.1.3. Self-Training...158 // 5.1.4. Transductive Support Vector Machines...159 // 5.1.5. Graph-Based Methods...160 // 5.1.6. Discussion...164 // 5.2. Learning from Positive and Unlabeled Examples ...165 // 5.2.1. Applications of PU Learning...165 // 5.2.2. Theoretical Foundation ...168 // 5.2.3. Building Classifiers: Two-Step Approach...169 // 5.2.4. Building Classifiers: Direct Approach...175 // 5.2.5. Discussion...178 // Appendix: Derivation of EM for Naive Bayesian Classification - 179

// Bibliographic Notes...181 // Part II: Web Mining // 6. Information Retrieval and Web Search... 183 // 6.1. Basic Concepts of Information Retrieval ...184 // 6.2. Information Retrieval Models...187 // 6.2.1. Boolean Model...188 // 6.2.2. Vector Space Model...188 // 6.2.3. Statistical Language Model...191 // 6.3. Relevance Feedback...192 // 6.4. Evaluation Measures...195 // 6.5. Text and Web Page Pre-Processing...199 // 6.5.1. Stopword Removal...199 // 6.5.2. Stemming...200 // 6.5.3. Other Pre-Processing Tasks for Text ...200 // 6.5.4. Web Page Pre-Processing...201 // 6.5.5. Duplicate Detection...203 // 6.6. Inverted Index and Its Compression ...204 // 6.6.1. Inverted Index ...204 // 6.6.2. Search Using an Inverted Index ...206 // 6.6.3. Index Construction...207 // 6.6.4. Index Compression ...209 // Table of Contents // XV // 6.7. Latent Semantic Indexing ...215 // 6.7.1. Singular Value Decomposition...215 // 6.7.2. Query and Retrieval ...218 // 6.7.3. An Example...219 // 6.7.4. Discussion...221 // 6.8. Web Search ...222 // 6.9. Meta-Search: Combining Multiple Rankings ...225 // 6.9.1. Combination Using Similarity Scores...226 // 6.9.2. Combination Using Rank Positions...227 // 6.10. Web Spamming ...229 // 6.10.1. Content Spamming ...230 // 6.10.2. Link Spamming ...231 // 6.10.3. Hiding Techniques ...233 // 6.10.4. Combating Spam ...234 // Bibliographic Notes...235 // Link Analysis ... 237 // 7.1. Social Network Analysis...238 // 7.1.1 Centrality ...238 // 7.1.2 Prestige ...241

// 7.2. Go-Citation and Bibliographic Coupling...243 // 7.2.1. Go-Citation ...244 // 7.2.2. Bibliographic Coupling ...245 // 7.3. PageRank...245 // 7.3.1. PageRank Algorithm ...246 // 7.3.2. Strengths and Weaknesses of PageRank...253 // 7.3.3. Timed PageRank...254 // 7.4. HITS ...255 // 7.4.1. HITS Algorithm...256 // 7.4.2. Finding Other Eigenvectors ...259 // 7.4.3. Relationships with Co-Citation and Bibliographic // Coupling ...259 // 7.4.4. Strengths and Weaknesses of HITS...260 // 7.5. Community Discovery ...261 // 7.5.1. Problem Definition...262 // 7.5.2. Bipartite Core Communities...264 // 7.5.3. Maximum Flow Communities...265 // 7.5.4. Email Communities Based on Betweenness...268 // 7.5.5. Overlapping Communities of Named Entities ...270 // XVI Table of Contents // Bibliographic Notes...271 // 8. Web Crawling...273 // 8.1. A Basic Crawler Algorithm...274 // 8.1.1. Breadth-First Crawlers...275 // 8.1.2. Preferential Crawlers ...276 // 8.2. Implementation Issues ...277 // 8.2.1. Fetching ...277 // 8.2.2. Parsing...278 // 8.2.3. Stopword Removal and Stemming ...280 // 8.2.4. Link Extraction and Canonicalization ...280 // 8.2.5. Spider Traps...282 // 8.2.6. Page Repository ...283 // 8.2.7. Concurrency...284 // 8.3. Universal Crawlers...285 // 8.3.1. Scalability...286 // 8.3.2. Coverage vs Freshness vs Importance ...288 // 8.4. Focused Crawlers...289 // 8.5. Topical Crawlers...292 // 8.5.1. Topical Locality and Cues...294 // 8.5.2. Best-First Variations...300 // 8.5.3. Adaptation...303

// 8.6. Evaluation...310 // 8.7. Crawler Ethics and Conflicts ...315 // 8.8. Some New Developments ...318 // Bibliographic Notes...320 // 9. Structured Data Extraction: Wrapper Generation â–  323 // 9.1 Preliminaries...324 // 9.1.1. Two Types of Data Rich Pages ...324 // 9.1.2. Data Model...326 // 9.1.3. HTML Mark-Up Encoding of Data Instances ...328 // 9.2. Wrapper Induction...330 // 9.2.1. Extraction from a Page ...330 // 9.2.2. Learning Extraction Rules...333 // 9.2.3. Identifying Informative Examples...337 // 9.2.4. Wrapper Maintenance...338 // Table of Contents // XVII // 9.3. Instance-Based Wrapper Learning...338 // 9.4. Automatic Wrapper Generation: Problems ...341 // 9.4.1. Two Extraction Problems ...342 // 9.4.2. Patterns as Regular Expressions...343 // 9.5. String Matching and Tree Matching ...344 // 9.5.1. String Edit Distance ...344 // 9.5.2. Tree Matching ...346 // 9.6. Multiple Alignment...350 // 9.6.1. Center Star Method...350 // 9.6.2. Partial Tree Alignment ...351 // 9.7. Building DOM Trees ...356 // 9.8. Extraction Based on a Single List Page: // Flat Data Records ...357 // 9.8.1. Two Observations about Data Records...358 // 9.8.2. Mining Data Regions...359 // 9.8.3. Identifying Data Records in Data Regions...364 // 9.8.4. Data Item Alignment and Extraction ...365 // 9.8.5. Making Use of Visual Information ...366 // 9.8.6. Some Other Techniques ...366 // 9.9. Extraction Based on a Single List Page: // Nested Data Records ...367 // 9.10. Extraction Based on

Multiple Pages ...373 // 9.10.1. Using Techniques in Previous Sections ...373 // 9.10.2. RoadRunner Algorithm ...374 // 9.11. Some Other Issues...375 // 9.11.1. Extraction from Other Pages...375 // 9.11.2. Disjunction or Optional...376 // 9.11.3. A Set Type or a Tuple Type...377 // 9.11.4. Labeling and Integration ...378 // 9.11.5. Domain Specific Extraction ...378 // 9.12. Discussion ...379 // Bibliographic Notes...379 // 10. Information Integration... 381 // 10.1. Introduction to Schema Matching ...382 // 10.2. Pre-Processing for Schema Matching...384 // 10.3. Schema-Level Match ...385 // 47*7 // woe // Noe // -- // I* , . . // -1 (J, Are t r // ein *( // zsofConien” ... // , Linguistic ApproaCheSroaches 1832 consrant Basedeve Matching // 1 . and Instancepomain al . 4 ... // V . . similarities // Combining S ... // 1m Match " // Other Issues // of Previous Match Results // Large Number of Schemas // 9 // 3 // 10.7.1. Reuse , // 10.7.2. Matching datch Results // Schema Matcl // 10.7.2 Userinteractions- // 10.7.of Web Query Interfaces // DB Integration 0 . - Based Approach 5 108.1. A Clustering EgSeed Approach,2 A Correlation based PP o // 10.8.2. Ah instance Based ApproachI. // 2 // 10.3.3 An instdre -•• // ID Constructing a Unified Global Query Interface // 10.9.1 Structural Appropriateness and the // Merge Algorithm // 1D.9.2. Lexical Appropriateness... // 10.9.3. Instance Appropriateness... // * 385 // 386 // - 387 // 390 // -. 391 // -. 392 // - 392 // ..

393 // - 393 // -. 394 // 394 // - 397 // -400 // -. 403 // ... 406 // Bibliographic Notes... // -a // 406 // 408 // 409 // 410 // 11 Dminion Mining // 411 // 11. Sentiment Classification ... 414 // 1111 Classification Based on Sentiment Phrases ...413 // 1112 Classification Using Text Classification Methods • 415 // � 1.3 Classification Using a Score Function ... 416 // 2 Feature-Based Opinion Mining and Summarization - 417 // Problem Definition ... 418 // 1122 Object Feature Extraction... // 22 Feature Extraction from Pros and Cons // of Format 1... // * Me // A 0 // 1 % // 1 VA // Feature Extraction from Reviews of // of Formats 2 and 3... // Opinion Orientation Classification // ---•• VVIIVA’VII // r // . vrative Sentence and Relation Mining // . :. roolemn Definition ... // So : cation of Gradable Comparative // 424 // 425 // 429 // 430 // 432 // 433 // 435 // Table of Contents // XIX // 11.3.3. Extraction of Comparative Relations...437 // 11.4. Opinion Search...439 // 11.5. Opinion Spam...441 // 11.5.1. Objectives and Actions of Opinion Spamming .441 // 11.5.2. Types of Spam and Spammers...442 // 11.5.3. Hiding Techniques...443 // 11.5.4. Spam Detection ...444 // Bibliographic Notes...446 // 12. Web Usage Mining ...449 // 12.1 . Data Collection and Pre-Processing...450 // 12.1.1 Sources and Types of Data...452 // 12.1.2 Key Elements of Web Usage Data // Pre-Processing ...455 // 12.2 Data Modeling for Web Usage Mining ...462 // 12.3 Discovery

and Analysis of Web Usage Patterns ...466 // 12.3.1. Session and Visitor Analysis ...466 // 12.3.2. Cluster Analysis and Visitor Segmentation .467 // 12.3.3 Association and Correlation Analysis...471 // 12.3.4 Analysis of Sequential and Navigational Patterns 475 // 12.3.5. Classification and Prediction Based on Web User // Transactions...479 // 12.4 . Discussion and Outlook...482 // Bibliographic Notes...482 // References ...485 // Index // 517