Web communities : analysis and construction
著者
書誌事項
Web communities : analysis and construction
Springer, 2006
大学図書館所蔵 全3件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
内容説明・目次
内容説明
Due to the lack of a uniform schema for Web documents and the sheer amount and dynamics of Web data, both the effectiveness and the efficiency of information management and retrieval of Web data is often unsatisfactory when using conventional data management techniques.
Web community, defined as a set of Web-based documents with its own logical structure, is a flexible and efficient approach to support information retrieval and to implement various applications. Zhang and his co-authors explain how to construct and analyse Web communities based on information like Web document contents, hyperlinks, or user access logs. Their approaches combine results from Web search algorithms, Web clustering methods, and Web usage mining. They also detail the necessary preliminaries needed to understand the algorithms presented, and they discuss several successful existing applications.
Researchers and students in information retrieval and Web search find in this all the necessary basics and methods to create and understand Web communities. Professionals developing Web applications will additionally benefit from the samples presented for their own designs and implementations.
目次
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 -- 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
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