Beschreibung
InhaltsangabeChapter 1: Introduction (10 pages) -- Web Search, -- Information Filtering -- Web Community Chapter 2: Preliminaries (30 pages) -- Statistics -- Similarity -- Markov Model -- Matrix Expression of Hyperlinks -- Eigenvector, Principle Engenvector, Secondary Engenvector -- Singular Value Decomposition (SVD) of Matrix -- Graph Theory Basis (Random walk) Chapter 3: HITS and Related Algorithms (50 pages) -- The Original HITS -- The Stability issues -- The Randomized HITS -- The Subspace HITS -- Weighted HITS -- Vector Space Model (VSM) -- Cover Density Ranking (CDR) -- The In-depth Analysis of the HITS -- HITS Improvement (a significant improvement to clever algorithm) -- Noise Page Elimination Algorithm Based on SVD -- The PHITS algorithm (probabilistic HITS) -- SALSA (Stochastic algorithm) -- Random Walks and the Kleinberg Algorithm Chapter 4: PageRank Related Algorithms (50 pages) -- The Original PageRank -- Probability Combination of Link and Content Information in PageRank -- Topic-Sensitve PageRank -- Search-Order: Breadth-First, Backlink, Random -- Quadratic Extrapolation -- Exporing the Block Structure of the Web for Computing PageRank -- Second Eignevalue of the Google Matrix -- A Latent Linkage Information (LLI) Algorithm -- WebPage Scoring Systems (WPSS) -- Rank Aggregation -- Random Suffer Method -- Voting Model -- SimRank (graph-based) -- When Experts Agree: Using Non-Affliated Experts to Rank Popular Topics -- PageRank, HITS and a Unified Framework for Link Analysis Chapter 5: Web Classification and Clustering (50 pages) -- Web Document Similarity Measurement -- Web Document Classification Based on Hyperlinks and Document Semantics -- Clustering Hypertext with Applications to Web Search -- Link-based Clustering to Improve Web Search Results -- Measure Similarity of Interest for Clustering Web-Users -- Clustering of Web Users Using Session-based Similarity Measures -- Scalable Techniques for Clustering the Web -- Clustering web surfers with mixtures of hidden Markov Models -- Clustering User Queries of a Search Engine -- Using Web Structure for Classifying and Describing Web Pages -- Matrix-Based Hierarchical Clustering Algorithms Chapter 6: Web Log/Content Mining for Web Community (50 pages) -- Cut-and-Pick Transactions for Proxy Log Mining -- Mining Web Logs to Improve Website Organization -- Extracting Large-Scale Knowledge Bases from the Web -- Mining the Space of Graph Properties -- Discovering Test Set Regularities in Relational Domains (classification) -- Enhanced Hypertext Categorization Using Hyperlinks -- The Structure of Broad Topics on the Web -- Discovering Unexpected Information from Your Competitors' Web Sites -- On Integrating Catalogs -- Web Community Mining and Web Log Mi
Inhaltsverzeichnis
Chapter 1: Introduction (10 pages) -- Web Search, -- Information Filtering -- Web Community Chapter 2: Preliminaries (30 pages) -- Statistics -- Similarity -- Markov Model -- Matrix Expression of Hyperlinks -- Eigenvector, Principle Engenvector, Secondary Engenvector -- Singular Value Decomposition (SVD) of Matrix -- Graph Theory Basis (Random walk) Chapter 3: HITS and Related Algorithms (50 pages) -- The Original HITS -- The Stability issues -- The Randomized HITS -- The Subspace HITS -- Weighted HITS -- Vector Space Model (VSM) -- Cover Density Ranking (CDR) -- The In-depth Analysis of the HITS -- HITS Improvement (a significant improvement to clever algorithm) -- Noise Page Elimination Algorithm Based on SVD -- The PHITS algorithm (probabilistic HITS) -- SALSA (Stochastic algorithm) -- Random Walks and the Kleinberg Algorithm Chapter 4: PageRank Related Algorithms (50 pages) -- The Original PageRank -- Probability Combination of Link and Content Information in PageRank -- Topic-Sensitve PageRank -- Search-Order: Breadth-First, Backlink, Random -- Quadratic Extrapolation -- Exporing the Block Structure of the Web for Computing PageRank -- Second Eignevalue of the Google Matrix -- A Latent Linkage Information (LLI) Algorithm -- Web Page Scoring Systems (WPSS) -- Rank Aggregation -- Random Suffer Method -- Voting Model -- SimRank (graph-based) -- When Experts Agree: Using Non-Affliated Experts to Rank Popular Topics -- PageRank, HITS and a Unified Framework for Link Analysis Chapter 5: Web Classification and Clustering (50 pages) -- Web Document Similarity Measurement -- Web Document Classification Based on Hyperlinks and Document Semantics -- Clustering Hypertext with Applications to Web Search -- Link-based Clustering to Improve Web Search Results -- Measure Similarity of Interest for Clustering Web-Users -- Clustering of Web Users Using Session-based Similarity Measures -- Scalable Techniques for Clustering the Web -- Clustering web surfers with mixtures of hidden Markov Models -- Clustering User Queries of a Search Engine -- Using Web Structure for Classifying and Describing Web Pages -- Matrix-Based Hierarchical Clustering Algorithms Chapter 6: Web Log/Content Mining for Web Community (50 pages) -- Cut-and-Pick Transactions for Proxy Log Mining -- Mining Web Logs to Improve Website Organization -- Extracting Large-Scale Knowledge Bases from the Web -- Mining the Space of Graph Properties -- Discovering Test Set Regularities in Relational Domains (classification) -- Enhanced Hypertext Categorization Using Hyperlinks -- The Structure of Broad Topics on the Web -- Discovering Unexpected Information from Your Competitors' Web Sites -- On Integrating Catalogs -- Web Community Mining and Web Log Mining: Commodity Cluster Based Execution -- Building Cybercommunity Hierarchy -- Automatic Topic Identification Using Webpage Clustering -- A Special Method to Separate Disconnected and Nearly-Disconnected Web Graph Components Chapter 7: Web Community Applications (50 pages) -- Link Structure Analysis for Finding Authoritative Images -- Integrating DOM with Hyperlinks for Enhanced Topic Distillation and Information Extraction -- What is the Page Known for? Computing Web Page Reputations -- Retrieving and Organizing Web Pages by Information Unit
