Part I: Data Mining Foundations

Transcription

1 Table of Contents 1. Introduction What is the World Wide Web? A Brief History of the Web and the Internet Web Data Mining What is Data Mining? What is Web Mining? Summary of Chapters How to Read this Book 11 Bibliographic Notes 12 Bibliography 13 Part I: Data Mining Foundations 2. Association Rules and Sequential Patterns Basic Concepts of Association Rules Apriori Algorithm Frequent Itemset Generation Association Rule Generation Data Formats for Association Rule Mining Mining with Multiple Minimum Supports Extended Model Mining Algorithm Rule Generation Mining Class Association Rules Problem Definition Mining Algorithm Mining with Multiple Minimum Supports 41

2 VI Table of Contents 2.6. Basic Concepts of Sequential Patterns Mining Sequential Patterns Based on GSP GSP Algorithm Mining with Multiple Minimum Supports Mining Sequential Patterns Based on PrefixSpan PrefixSpan Algorithm Mining with Multiple Minimum Supports Generating Rules from Sequential Patterns Sequential Rules Label Sequential Rules Class Sequential Rules 55 Bibliographic Notes 56 Bibliography Supervised Learning Basic Concepts Decision Tree Induction Learning Algorithm Impurity Function Handling of Continuous Attributes Some Other Issues Classifier Evaluation Evaluation Methods Precision, Recall, F-score and Breakeven Point Receiver Operating Characteristic Curve Lift Curve Rule Induction Sequential Covering Rule Learning: Learn-One-Rule Function Discussion Classification Based on Associations Classification Using Class Association Rules Class Association Rules as Features Classification Using Normal Association Rules Naïve Bayesian Classification Naïve Bayesian Text Classification Probabilistic Framework Naïve Bayesian Model Discussion 108

3 Table of Contents VII 3.8. Support Vector Machines Linear SVM: Separable Case Linear SVM: Non-Separable Case Nonlinear SVM: Kernel Functions K-Nearest Neighbor Learning Ensemble of Classifiers Bagging Boosting 126 Bibliographic Notes 127 Bibliography Unsupervised Learning Basic Concepts K-means Clustering K-means Algorithm Disk Version of the K-means Algorithm Strengths and Weaknesses Representation of Clusters Common Ways of Representing Clusters Clusters of Arbitrary Shapes Hierarchical Clustering Single-Link Method Complete-Link Method Average-Link Method Strengths and Weaknesses Distance Functions Numeric Attributes Binary and Nominal Attributes Text Documents Data Standardization Handling of Mixed Attributes Which Clustering Algorithm to Use? Cluster Evaluation Discovering Holes and Data Regions 162 Bibliographic Notes 165 Bibliography 166

4 VIII Table of Contents 5. Partially Supervised Learning Learning from Labeled and Unlabeled Examples EM Algorithm with Naïve Bayesian Classification Co-Training Self-Training Transductive Support Vector Machines Graph-Based Methods Discussion Learning from Positive and Unlabeled Examples Applications of PU Learning Theoretical Foundation Building Classifiers: Two-Step Approach Building Classifiers: Biased-SVM Building Classifiers: Probability Estimation Discussion 201 Appendix: Derivation of EM for Naïve Bayesian Classification 202 Bibliographic Notes 204 Bibliography 206 Part II: Web Mining 6. Information Retrieval and Web Search Basic Concepts of Information Retrieval Information Retrieval Models Boolean Model Vector Space Model Statistical Language Model Relevance Feedback Evaluation Measures Text and Web Page Pre-Processing Stopword Removal Stemming Other Pre-Processing Tasks for Text Web Page Pre-Processing Duplicate Detection Inverted Index and Its Compression 232

5 Table of Contents IX Inverted Index Search Using an Inverted Index Index Construction Index Compression Latent Semantic Indexing Singular Value Decomposition Query and Retrieval An Example Discussion Web Search Meta-Search: Combining Multiple Rankings Combination Using Similarity Scores Combination Using Rank Positions Web Spamming Content Spamming Link Spamming Hiding Techniques Combating Spam 262 Bibliographic Notes 263 Bibliography Social Network Analysis Social Network Analysis Centrality Prestige Co-Citation and Bibliographic Coupling Co-Citation Bibliographic Coupling PageRank PageRank Algorithm Strengths and Weaknesses of PageRank Timed PageRank and Recency Search HITS HITS Algorithm Finding Other Eigenvectors Relationships with Co-Citation and Bibliographic Coupling Strengths and Weaknesses of HITS Community Discovery 294

6 X Table of Contents Problem Definition Bipartite Core Communities Maximum Flow Communities Communities Based on Betweenness Overlapping Communities of Named Entities 302 Bibliographic Notes 304 Bibliography Web Crawling A Basic Crawler Algorithm Breadth-First Crawlers Preferential Crawlers Implementation Issues Fetching Parsing Stopword Removal and Stemming Link Extraction and Canonicalization Spider Traps Page Repository Concurrency Universal Crawlers Scalability Coverage vs Freshness vs Importance Focused Crawlers Topical Crawlers Topical Locality and Cues Best-First Variations Adaptation Evaluation Crawler Ethics and Conflicts Some New Developments 356 Bibliographic Notes 358 Bibliography Structured Data Extraction: Wrapper Generation Preliminaries Two Types of Data Rich Pages Data Model 366

7 Table of Contents XI HTML Mark-Up Encoding of Data Instances Wrapper Induction Extraction from a Page Learning Extraction Rules Identifying Informative Examples Wrapper Maintenance Instance-Based Wrapper Learning Automatic Wrapper Generation: Problems Two Extraction Problems Patterns as Regular Expressions String Matching and Tree Matching String Edit Distance Tree Matching Multiple Alignment Center Star Method Partial Tree Alignment Building DOM Trees Extraction Based on a Single List Page: Flat Data Records Two Observations about Data Records Mining Data Regions Identifying Data Records in Data Regions Data Item Alignment and Extraction Making Use of Visual Information Some Other Techniques Extraction Based on a Single List Page: Nested Data Records Extraction Based on Multiple Pages Using Techniques in Previous Sections RoadRunner Algorithm Some Other Issues Extraction from Other Pages Disjunction or Optional A Set Type or a Tuple Type Labeling and Integration Domain Specific Extraction Discussion 419 Bibliographic Notes 419 Bibliography 421

8 XII Table of Contents 10. Information Integration Introduction to Schema Matching Pre-Processing for Schema Matching Schema-Level Match Linguistic Approaches Constraint Based Approaches Domain and Instance-Level Matching Combining Similarities :m Match Some Other Issues Reuse of Previous Match Results Matching a Large Number of Schemas Schema Match Results User Interactions Integration of Web Query Interfaces A Clustering Based Approach A Correlation Based Approach An Instance Based Approach Constructing a Unified Global Query Interface Structural Appropriateness and the Merge Algorithm Lexical Appropriateness Instance Appropriateness 453 Bibliographic Notes 454 Bibliography Opinion Mining and Sentiment Analysis The Problem of Opinion Mining Problem Definitions Aspect-Based Opinion Summary Document Sentiment Classification Classification Based on Supervised Learning Classification Based on Unsupervised Learning Sentence Subjectivity and Sentiment Classification Opinion Lexicon Expansion Aspect-Based Opinion Mining Aspect Sentiment Classification 481

9 Table of Contents XIII Basic Rules of Opinions Aspect Extraction Simultaneous Opinion Lexicon Expansion and Aspect Extraction Mining Comparative Opinions Problem Definitions Identification of Comparative Sentences Identification of Preferred Entities Some Other Problems Opinion Search and Retrieval Opinion Spam Detection Types of Spam and Spammers Hiding Techniques Spam Detection Based on Supervised Learning Spam Detection Based on Abnormal Behaviors Group Spam Detection Utility of Reviews 514 Bibliographic Notes 515 Bibliography Web Usage Mining Data Collection and Pre-Processing Sources and Types of Data Key Elements of Web Usage Data Pre-Processing Data Modeling for Web Usage Mining Discovery and Analysis of Web Usage Patterns Session and Visitor Analysis Cluster Analysis and Visitor Segmentation Association and Correlation Analysis Analysis of Sequential and Navigational Patterns Classification and Prediction Based on Web User Transactions Recommender Systems and Collaborative Filtering The Problem of Recommendation Content-Based Recommendation Collaborative Filtering: K-Nearest Neighbor Collaborative Filtering: Using Association Rules Collaborative Filtering: Matrix Factorization 565

10 XIV Table of Contents Query Log Mining Data Sources, Characteristics and Challenges Query Log Data Preparation Query Log Data Models Query Log Feature Extraction Query Log Mining Applications Query Log Mining Methods Computational Advertising Discussion and Outlook 593 Bibliographic Notes 593 Bibliography 595 Subject Index 605