COWES: Clustering Web Users Based on Historical Web Sessions

  • Ling Chen
  • Sourav S. Bhowmick
  • Jinyan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by clustering web users based on the evolutions of their historical web sessions. We present an algorithm called COWES to cluster web users in three steps. First, given a set of web users, we mine the history of their web sessions to extract interesting patterns that capture the characteristics of their usage data evolution. Then, the similarity between web users is computed based on their common interesting patterns. Then, the desired clusters are generated by a partitioning clustering technique. Web user clusters generated based on their historical web sessions are useful in intelligent web advertisement and web caching.


Association Rule Element Similarity Strength Similarity Proxy Cache Cache Region 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Buchwalter, C., Ryan, M., Martin, D.: The state of online advertising: data covering 4th \(\textsc{Q}\) 2000. In: TR Adrelevance (2001)Google Scholar
  2. 2.
    Cao, P., Irani, S.: Cost-aware www proxy caching algorithms. In: Proc. of USENIX SITSY. (1997)Google Scholar
  3. 3.
    Chen, L., Bhowmick, S.S., Chia, L.T.: Mining association rules from structural deltas of historical xml documents. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. In: Knowledge and Information Systems, vol. 1 (1999)Google Scholar
  5. 5.
    Fu, Y., Sandhu, K., Shih, M.: A generalization-based approach to clustering of web usage sessions. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Kaufman, L., Pousseeuw, P.: Finding groups in data: An introduction to cluster analysis. John Wiley and Sons, Chichester (1990)CrossRefGoogle Scholar
  7. 7.
    Li, T., Yang, Q., Wang, K.: Classification pruning for web-request prediction. In: Proc. of WWW (2001)Google Scholar
  8. 8.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Proc. of WIDM (2001)Google Scholar
  9. 9.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Cheung, D.W.-L., Mamoulis, N., Yiu, S.-M.: An efficient and scalable algorithm for clustering xml documents by structure. IEEE TKDE 16, 82–96 (2004)Google Scholar
  11. 11.
    Wang, W., Zaiane, O.R.: Clustering web sessions by sequence alignment. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Xiao, J., Zhang, Y.: Clustering of web users using session-based similarity measures. In: Proc. of ICCNMC 2001 (2001)Google Scholar
  13. 13.
    Yang, Q., Zhang, H.H., Li, T.: Mining web logs for predicition models in www caching and prefetching. In: Proc. of ACM SIGKDD (2001)Google Scholar
  14. 14.
    Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proc. of CIKM (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Chen
    • 1
    • 2
  • Sourav S. Bhowmick
    • 1
  • Jinyan Li
    • 2
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Institute for Infocomm ResearchSingapore

Personalised recommendations