Lessons from the Application of Domain-Independent Data Mining System for Discovering Web User Access Patterns

  • Leszek Borzemski
  • Adam Druszcz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)


This paper presents the usage of a general domain-independent data mining system in discovering of the Web user access patterns. DB2 Intelligent Miner for Data was successfully used in data mining of a huge Web log which was collected during the World FIFA Cup 1998. The clustering, associations and sequential pattern mining functions were considered in the context of Web usage mining. The clustering method was found the most profitable and the discovered usage patterns can be used in Web personalization and recommendation systems.


Association Rule Sequential Pattern Proxy Server Mining Function User Session 
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.
    Albanese, M., Picariello, A., Sansone, C., Sansone, L.: A Web Personalization System based on Web Usage Mining Techniques. In: WWW 2004. ACM Press, New York (2004)Google Scholar
  2. 2.
    Arlit, M., Jin, T.: A Workload Characterization Study of the 1998 World Cup Web Site. IEEE Network, 300–373 (May-June 2000)Google Scholar
  3. 3.
    Borzemski, L.: Data Mining in Evaluation of Internet Path Performance. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS, vol. 3029, pp. 643–652. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Chakrabarti, S.: Mining the Web: Analysis of Hypertext and Semi Structured Data. Morgan Kaufmann, San Francisco (2003) Google Scholar
  5. 5.
    Chen, M.-S., Park, J.S., Yu, P.S.: Efficient Mining Date for Path Traversal Patterns in Distributed Systems. In: 16th IEEE Int. Conf. on Distributed Computing Systems (1996)Google Scholar
  6. 6.
    Fu, Y., Sandhu, K., Shi, M.-Y.: Clustering of Web Users Based on the Access Patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS, vol. 1836. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Fürnkranz, J.: Web mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 899–920. Springer, Berlin (2005)CrossRefGoogle Scholar
  8. 8.
    Mobasher, B.: Web Usage Mining and Personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing. CRC Press, Boca Raton (2005)Google Scholar
  9. 9.
    Spiliopoulou, M., Faulstich, L.C.: WUM: A Tool for Web Utilization Analysis. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 184–203. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  10. 10.
    Using Intelligent Miner for Data. V8 Rel. 1, IBM Redbooks, SH12-6750-00 (2002) Google Scholar
  11. 11.
    Wu, K.L., Yu, P.S., Ballman, A.: Speed Tracer: A Web Usage Mining and Analysis Tool. IBM Systems Journal 37(1) (1998)Google Scholar
  12. 12.
    Zhang, F., Chang, H.: Research and Development in Web Usage Mining System–Key Issues and Proposed Solutions: A Survey. In: Proc. First IEEE Int. Conf. on Machine Learning and Cybernetics, pp. 986–990 (2002)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leszek Borzemski
    • 1
  • Adam Druszcz
    • 1
  1. 1.Institute of Information Science and EngineeringWroclaw University of TechnologyWroclawPoland

Personalised recommendations