Web data mining: exploring hyperlinks, contents, and usage data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2008
|
| Ausgabe: | corr. 2. print. |
| Schriftenreihe: | Data-centric systems and applications
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. [486] - 515 |
| Beschreibung: | XIX, 532 S. Ill., graph. Darst. |
| ISBN: | 9783540378815 3540378812 |
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Datensatz im Suchindex
| _version_ | 1804139298745745408 |
|---|---|
| adam_text | Titel: Web data mining
Autor: Liu, Bing
Jahr: 2008
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
7. Link Analysis 237
7.1. Social Network Analysis 238
7.1.1 Centrality 238
7.1.2 Prestige ¦ 241
7.2. Co-Citation and Bibliographic Coupling 243
7.2.1. Co-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 ¦ 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
XVIII Table of Contents
10.3.1. Linguistic Approaches 385
10.3.2. Constraint Based Approaches 386
10.4. Domain and Instance-Level Matching 387
10.5. Combining Similarities 390
10.6. 1:m Match 391
10.7. Some Other Issues 392
10.7.1. Reuse of Previous Match Results 392
10.7.2. Matching a Large Number of Schemas 393
10.7.3 Schema Match Results 393
10.7.4 User Interactions 394
10.8. Integration of Web Query Interfaces 394
10.8.1. A Clustering Based Approach 397
10.8.2. A Correlation Based Approach 400
10.8.3. An Instance Based Approach 403
10.9. Constructing a Unified Global Query Interface 406
10.9.1. Structural Appropriateness and the
Merge Algorithm 406
10.9.2. Lexical Appropriateness 408
10.9.3. Instance Appropriateness 409
Bibliographic Notes 410
11. Opinion Mining 411
11.1. Sentiment Classification 412
11.1.1. Classification Based on Sentiment Phrases 413
11.1.2. Classification Using Text Classification Methods ¦ 415
11.1.3. Classification Using a Score Function 416
11.2. Feature-Based Opinion Mining and Summarization ¦¦ 417
11.2.1. Problem Definition 418
11.2.2. Object Feature Extraction 424
11.2.3. Feature Extraction from Pros and Cons
of Format 1 425
11.2.4. Feature Extraction from Reviews of
of Formats 2 and 3 429
11.2.5. Opinion Orientation Classification 430
11.3. Comparative Sentence and Relation Mining 432
11.3.1. Problem Definition 433
11.3.2. Identification of Gradable Comparative
Sentences 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
|
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| discipline | Informatik |
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| id | DE-604.BV035625646 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:41:52Z |
| institution | BVB |
| isbn | 9783540378815 3540378812 |
| language | English |
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| series2 | Data-centric systems and applications |
| spelling | Liu, Bing Verfasser aut Web data mining exploring hyperlinks, contents, and usage data Bing Liu corr. 2. print. Berlin [u.a.] Springer 2008 XIX, 532 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Data-centric systems and applications Literaturverz. S. [486] - 515 World Wide Web - Data Mining Data mining Datamining. gtt Web databases World wide web. gtt World Wide Web (DE-588)4363898-3 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf World Wide Web (DE-588)4363898-3 s Data Mining (DE-588)4428654-5 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017680702&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Liu, Bing Web data mining exploring hyperlinks, contents, and usage data World Wide Web - Data Mining Data mining Datamining. gtt Web databases World wide web. gtt World Wide Web (DE-588)4363898-3 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4363898-3 (DE-588)4428654-5 |
| title | Web data mining exploring hyperlinks, contents, and usage data |
| title_auth | Web data mining exploring hyperlinks, contents, and usage data |
| title_exact_search | Web data mining exploring hyperlinks, contents, and usage data |
| title_full | Web data mining exploring hyperlinks, contents, and usage data Bing Liu |
| title_fullStr | Web data mining exploring hyperlinks, contents, and usage data Bing Liu |
| title_full_unstemmed | Web data mining exploring hyperlinks, contents, and usage data Bing Liu |
| title_short | Web data mining |
| title_sort | web data mining exploring hyperlinks contents and usage data |
| title_sub | exploring hyperlinks, contents, and usage data |
| topic | World Wide Web - Data Mining Data mining Datamining. gtt Web databases World wide web. gtt World Wide Web (DE-588)4363898-3 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | World Wide Web - Data Mining Data mining Datamining. Web databases World wide web. World Wide Web Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017680702&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT liubing webdataminingexploringhyperlinkscontentsandusagedata |