Skip to main content
Springer Nature Link
Log in

Data Mining of User Navigation Patterns

  • Conference paper
  • First Online:
Web Usage Analysis and User Profiling (WebKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1836))

Included in the following conference series:

  • 1236 Accesses

Abstract

We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as a hypertext probabilistic grammar whose higher probability strings correspond to the user’s preferred trails. An algorithm to efficiently mine such trails is given. We make use of the N gram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar’s statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar’s entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Azer Bestavros. Using speculation to reduce server load and service time on the www. In Proc. of the fourth ACM International Conference on Information and Knowledge Management, pages 403–410, Baltimore,MD, 1995.

    Google Scholar 

  2. J. Borges and M. Levene. Mining association rules in hypertext databases. In Proc. of the fourth Int. Conf. on Knowledge Discovery and Data Mining, pages 149–153, August 1998.

    Google Scholar 

  3. José Borges and Mark Levene. Heuristics for mining high quality user web navigation patterns. Research Note RN/99/68, Department of Computer Science, University College London, Gower Street, London, UK, October 1999.

    Google Scholar 

  4. Alex G. Büchner, M. Baumgarten, S.S. Anand, Maurice D. Mulvenna, and J.G. Hughes. Navigation pattern discovery from internet data. In Proc. of the Web Usage Analysis and User Profiling Workshop, pages 25–30, San Diego, California, August 1999.

    Google Scholar 

  5. Alex G. Büchner, Maurice D. Mulvenna, Sarab S. Anand, and John G. Hughes. An internet-enabled knowledge discovery process. In Proc. of 9th International Database Conference, pages 13–27, Hong Kong, July 1999.

    Google Scholar 

  6. Lara D. Catledge and James E. Pitkow. Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems, 27(6):1065–1073, April 1995.

    Google Scholar 

  7. Soumen Chakrabarti, Byron E. Dom, David Gibson, Jon Kleinberg, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew S. Tomkins. Mining the link structure of the world wide web. IEEE Computer, 32(8):60–67, August 1999.

    Google Scholar 

  8. E. Charniak. Statistical Language Learning. The MIT Press, 1996.

    Google Scholar 

  9. Christopher Chatfield. Statistical inferences regarding markov chain models. Applied Statistics, 22:7–20, 1973.

    Article  Google Scholar 

  10. M.-S. Chen, J. S. Park, and P. S. Yu. Efficient data mining for traversal patterns. IEEE Trans. on Knowledge and Data Eng., 10(2):209–221, March/April 1998.

    Google Scholar 

  11. R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1):5–32, February 1999.

    Google Scholar 

  12. T. Cover and J. Thomas. Elements of Information Theory. John Wiley & Sons, 1991.

    Google Scholar 

  13. M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery. Learning to extract symbolic knowledge from the world wide web. In Proc. of the 15th National Conf. on Artificial Intelligence, pages 509–516, July 1998.

    Google Scholar 

  14. W. Feller. An Introduction to Probability Theory and Its Applications. John Wiley & Sons, second edition, 1968.

    Google Scholar 

  15. J. Hopcroft and J. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, 1979.

    Google Scholar 

  16. M. Levene and G. Loizou. A probabilistic approach to navigation in hypertext. Information Sciences, 114:165–186, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  17. Venkata N. Padmanabhan and Jeffrey C. Mogul. Using predictive prefetching to improve world wide web latency. Computer Communications Review, 26, 1996.

    Google Scholar 

  18. Mike Perkowitz and Oren Etzioni. Adaptive web sites: an AI challenge. In Proc. of Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 16–21, Nagoya, Japan, August 1997.

    Google Scholar 

  19. Mike Perkowitz and Oren Etzioni. Adaptive sites: Automatically synthesizing web pages. In Proc. of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pages 727–732, Madison, Wisconsin, July 1998.

    Google Scholar 

  20. Peter L.T. Pirolli and James E. Pitkow. Distributions of surfers’ paths through the world wide web: Empirical characterizations. World Wide Web, 2:29–45, 1999.

    Article  Google Scholar 

  21. L. Rosenfeld and P. Morville. Information Architecture for the World Wide Web. O’Reilly, 1998.

    Google Scholar 

  22. S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict http requests. Computer Networks and ISDN Systems, 30:457–467, 1998.

    Article  Google Scholar 

  23. M. Spiliopoulou, L. C. Faulstich, and K. Wilkler. A data miner analyzing the navigational behaviour of web users. In Proc. of the Workshop on Machine Learning in User Modelling of the ACAI99, Greece, July 1999.

    Google Scholar 

  24. Myra Spiliopoulou and Lukas C. Faulstich. WUM: a tool for web utilization analysis. In Proc. of the International Workshop on the Web and Databases (WebDB’98), pages 184–203, Valencia, Spain, March 1998.

    Google Scholar 

  25. R. Stout. Web Site Stats: tracking hits and analyzing traffic. Osborne McGraw-Hill, 1997.

    Google Scholar 

  26. C. S. Wetherell. Probabilistic languages: A review and some open questions. Computing Surveys, 12(4):361–379, December 1980.

    Google Scholar 

  27. T. W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Proc. of the 5th Int. World Wide Web Conference, pages 1007–1014, 1996.

    Google Scholar 

  28. O. R. Zaïane, M. Xin, and J. Han. Discovering web access patterns and trends by applying olap and data mining technology on web logs. In Proc. Advances in Digital Libraries Conf., pages 12–29, Santa Barbara, CA, April 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    José Borges & Mark Levene

Authors
  1. José Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Levene
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Discovery and Intelligent Agents Technology, Redwood Investment Systems Inc., Boston, MA, 02110-1225, USA

    Brij Masand (Director of Knowledge) (Director of Knowledge)

  2. Institut für Wirtschaftsinformatik, Humboldt Universität zu Berlin, Spandauer Str. 1, 10178, Berlin, Germany

    Myra Spiliopoulou

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Borges, J., Levene, M. (2000). Data Mining of User Navigation Patterns. In: Masand, B., Spiliopoulou, M. (eds) Web Usage Analysis and User Profiling. WebKDD 1999. Lecture Notes in Computer Science(), vol 1836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44934-5_6

Download citation

  • DOI: https://doi.org/10.1007/3-540-44934-5_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67818-2

  • Online ISBN: 978-3-540-44934-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics