Clustering Frequent Navigation Patterns from Website Logs by Using Ontology and Temporal Information

  • Sefa Kilic
  • Pinar Senkul
  • Ismail Hakki Toroslu
Conference paper


In this work, clustering algorithms are used in order to group similar frequent sequences of Web page visits. A new sequence is compared with all clusters and it is assigned to the most similar one. This work can be used for predicting and prefetching the next page user will visit or for helping the navigation of user in the website. They can also be used to improve the structure of website for easier navigation. In this study the effect of time spent on each web page during the session is also analyzed.


Frequent navigation patterns Clustering Ontology Web page recommendation Semantic similarity 



This work is supported by grant number TUBITAK-109E282, TUBITAK.


  1. 1.
    Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, pp. 33–40 (2001)Google Scholar
  2. 2.
    Facca, F.M., Lanzi, P.L.: Mining interesting knowledge from weblogs: a survey. Data Knowl. Eng. 53, 225–241 (2005).
  3. 3.
    Heflin, J., Hendler, J., Luke, S.: SHOE a knowledge representation language for Internet applications. Technical Report CS-TR-4078 (UMIACS TR-99-71), Department of Computer Science, University of Maryland (1999)Google Scholar
  4. 4.
    Karypis, G.: CLUTO—a clustering toolkit. Technical Report #02-017 (Nov 2003)Google Scholar
  5. 5.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43, 142–151 (2000). doi: 10.1145/345124.345169 Google Scholar
  6. 6.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19, 17–30 (1989)CrossRefGoogle Scholar
  7. 7.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. Newsl. 1, 12–23 (2000). doi: 10.1145/846183.846188
  8. 8.
    Yilmaz, H., Senkul, P.: Using ontology and sequence information for extracting behavior patterns from web navigation logs. In: IEEE, ICDM Workshop on Semantic Aspects in Data Mining (SADM’10) (Dec 2010)Google Scholar
  9. 9.
    Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach. Learn. 55(3), 311–331 (2004)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Sefa Kilic
    • 1
  • Pinar Senkul
    • 1
  • Ismail Hakki Toroslu
    • 1
  1. 1.METU Computer Engineering DepartmentAnkaraTurkey

Personalised recommendations