Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach

  • Guandong Xu
  • Yu Zong
  • Peter Dolog
  • Yanchun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6278)


Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects is an interesting topic in the context of Web usage mining, which is able to capture the underlying user navigational interest and content preference simultaneously. In this paper we will present an algorithm using bipartite spectral clustering to co-cluster Web users and pages. The usage data of users visiting Web sites is modeled as a bipartite graph and the spectral clustering is then applied to the graph representation of usage data. The proposed approach is evaluated by experiments performed on real datasets, and the impact of using various clustering algorithms is also investigated. Experimental results have demonstrated the employed method can effectively reveal the subset aggregates of Web users and pages which are closely related.


Bipartite Graph Spectral Cluster User Session Spectral Space User Access Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guandong Xu
    • 1
    • 2
  • Yu Zong
    • 2
  • Peter Dolog
    • 1
  • Yanchun Zhang
    • 2
  1. 1.Computer Science Department Selma Lagerlofs Vej 300IWIS - Intelligent Web and Information Systems, Aalborg UniversityAalborgDenmark
  2. 2.Center for Applied Informatics, School of Engineering & ScienceVictoria UniversityAustralia

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