Discovering Rich Navigation Patterns on a Web Site

  • Karine Chevalier
  • Cécile Bothorel
  • Vincent Corruble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)


In this paper, we describe a method for discovering knowledge about users on a web site from data composed of demographic descriptions and site navigations. The goal is to obtain knowledge that is useful to answer two types of questions: (1) how do site users visit a web site? (2) Who are these users? Our approach is based on the following idea: the set of all site users can be divided into several coherent subgroups; each subgroup shows both distinct personal characteristics, and a distinct browsing behaviour. We aim at obtaining associations between site usage patterns and personal user descriptions. We call this combined knowledge ’rich navigation patterns’. This knowledge characterizes a precise web site usage and can be used in several applications: prediction of site navigation, recommendations or improvement in site design.


User Profile User Characteristic Site Usage Frequent Sequence Cluster Phase 
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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  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB conference, Santiago, Chile (1994)Google Scholar
  2. 2.
    Beaudouin, V., Assadi, H., Beauvisage, T., Lelong, B., Licoppe, C., Ziemalicki, C., Arbues, L., Lendrevie, J.: Parcours sur Internet: analyse des traces d’usage. Rapport RP/FTR&D/7495, France Telecom R&D, Net Value, HEC (2002)Google Scholar
  3. 3.
    Borges, J., Levene, M.: Mining Association Rules in Hypertext Databases. In: Proceedings of Conference on Knowledge Discovery and Data Mining (1998)Google Scholar
  4. 4.
    Borges, J., Levene, M.: Data Mining of User Navigation Patterns. In: Proceedings of the Workshop on Web Usage Analysis and User Profiling, San Diego, CA, August 15, pp. 31–36 (1999)Google Scholar
  5. 5.
    Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information System 1(1), 5–32 (1999)Google Scholar
  6. 6.
    Cooley, R., Tan, P., Srivastava, J.: WebSIFT: The Web Site Information Filter System. In: Proceedings of the Web Usage Analysis and User Profiling Workshop (August 1999)Google Scholar
  7. 7.
    Cybermétrie. Cybermétrie La mesure collective des sites de l’Internet en France, Source: Médiamétrie,
  8. 8.
    Demiriz, A., Zaki, M.: webSPADE: A Parallel Sequence Mining Algorithm to Analyze the Web Log Data. Submitted to KDD 2002 (2002)Google Scholar
  9. 9.
    Fu, Y., Sandhu, K., Shih, M.: Clustering of Web users based on access patterns. In: proceedings of the 1999 KDD Workshop on Web Mining, San Diego (1999)Google Scholar
  10. 10.
    Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.: Frequent Pattern Projected Sequential Pattern Mining. In: Proceedings of international Conference on KDD, Boston (August 2000)Google Scholar
  11. 11.
    Hay, B., Wets, G., Vanhoof, K.: Clustering navigation patterns on a website using a Sequence Alignment Method. In: Proceedings of IJCAI’s Workshop on Intelligent Techniques for Web Personnalisation, Seattle, Washington, August 4–6 (2001)Google Scholar
  12. 12.
    Media Metrix mediametrix.htm (comScore),
  13. 13.
  14. 14.
  15. 15.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proceedings of ICDE 2001, Germany (April 2001)Google Scholar
  16. 16.
  17. 17.
    Zaki, M.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42(1), 31–60 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Karine Chevalier
    • 1
    • 2
  • Cécile Bothorel
    • 1
  • Vincent Corruble
    • 2
  1. 1.France Telecom R&D (Lannion)France
  2. 2.LIP6, Pole IAUniversité Pierre et Marie Curie (Paris VI)France

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