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Hierarchical co-clustering for web queries and selected URLs

Hosseini, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-76993-4_57
  3. Publisher: Springer Verlag , 2007
  4. Abstract:
  5. Recently query log mining is extensively used by web information systems. In this paper a new hierarchical co-clustering for queries and URLs of a search engine log is introduced. In this method, firstly we construct a bipartite graph for queries and visited URLs, and then to discover noiseless clusters, all queries and related URLs are projected in a reduced dimensional space by applying singular value decomposition. Finally, all queries and URLs are iteratively clustered for constructing hierarchical categorization. The method has been evaluated using a real world data set and shows promising results. © Springer-Verlag Berlin Heidelberg 2007
  6. Keywords:
  7. Cluster analysis ; Data mining ; Graph theory ; Query languages ; Singular value decomposition ; dimensional space ; Hierarchical categorization ; Hierarchical co-clustering ; Hierarchical systems
  8. Source: 8th International Conference on Web Information Systems Engineering, WISE 2007, Nancy, 3 December 2007 through 7 December 2007 ; Volume 4831 LNCS , 2007 , Pages 653-662 ; 03029743 (ISSN); 9783540769927 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-540-76993-4_57