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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Zhang, Y., Yu, J.X., Hou, J.: Web Communities: Analysis and Construction. Springer, Berlin (2006)Google Scholar
  2. 2.
    Wang, X., Zhai, C.: Learn from web search logs to organize search results. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 87–94. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Kummamuru, K., Lotlikar, R., Roy, S., Singal, K., Krishnapuram, R.: A hierarchical monothetic document clustering algorithm for summarization and browsing search results. In: WWW 2004: Proceedings of the 13th International Conference on World Wide Web, pp. 658–665. ACM, New York (2004)CrossRefGoogle Scholar
  4. 4.
    Flesca, S., Greco, S., Tagarelli, A., Zumpano, E.: Mining user preferences, page content and usage to personalize website navigation. World Wide Web Journal 8(3), 317–345 (2005)CrossRefGoogle Scholar
  5. 5.
    Haveliwala, T.H., Gionis, A., Indyk, P.: Scalable techniques for clustering the web (extended abstract). In: WebDB 2000, Third International Workshop on the Web and Databases in Conjunction with ACM SIGMOD (2000)Google Scholar
  6. 6.
    Ferragina, P., Gulli, A.: A personalized search engine based on web-snippet hierarchical clustering. In: WWW 2005: Special Interest Tracks and Posters of The 14th International Conference on World Wide Web, pp. 801–810. ACM, New York (2005)CrossRefGoogle Scholar
  7. 7.
    Hou, J., Zhang, Y.: Utilizing hyperlink transitivity to improve web page clustering. In: Proceedings of the 14th Australasian Database Conferences (ADC 2003), vol. 37, pp. 49–57. ACS Inc, Adelaide (2003)Google Scholar
  8. 8.
    Mobasher, B., Dai, H., Nakagawa, M., Luo, T.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6(1), 61–82 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Xu, G., Zhang, Y., Zhou, X.: A web recommendation technique based on probabilistic latent semantic analysis. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, J.-Y., Sheng, Q.Z. (eds.) WISE 2005. LNCS, vol. 3806, pp. 15–28. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD 2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–274. ACM, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Giannakidou, E., Koutsonikola, V.A., Vakali, A., Kompatsiaris, Y.: Co-clustering tags and social data sources. In: WAIM, pp. 317–324 (2008)Google Scholar
  12. 12.
    Hanisch, D., Zien, A., Zimmer, R., Lengauer, T.: Co-clustering of biological networks and gene expression data. In: ISMB, pp. 145–154 (2002)Google Scholar
  13. 13.
    Jin, X., Zhou, Y., Mobasher, B.: A maximum entropy web recommendation system: Combining collaborative and content features. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2005), Chicago, pp. 612–617 (2005)Google Scholar

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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