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Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 2703))

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Abstract

We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static user data can be incorporated easily to possibly enhance the labelling of users. Furthermore, we use prior knowledge to enhance generalization and avoid numerical problems. We use parameter tying to decrease the danger of overfitting and to reduce computational overhead. We put a flat prior on the parameters to deal with the problem that certain transitions between page categories occur very seldom or not at all, in order to ensure that a nonzero transition probability between these categories nonetheless remains. In applications to artificial data and real-world web logs we demonstrate the usefulness of our approach. We train a mixture of HMMs on artificial navigation patterns, and show that the correct model is being learned. Moreover, we show that the use of static ’satellite data’ may enhance the labeling of shorter navigation patterns. When applying a mixture of HMMs to real-world web logs from a large Dutch commercial web site, we demonstrate that sensible page categorizations are being learned.

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References

  1. Cadez, I., Gaffney, S., Smyth, P.: A general probabilistic framework for clustering individuals. Technical report, Univ. Calif., Irvine (March 2000)

    Google Scholar 

  2. Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Visualization of navigation patterns on a web site using model-based clustering. Technical report, Univ. Calif., Irvine (March 2000)

    Google Scholar 

  3. Cooley, R.W.: Web usage mining: discovery and application of interesting patterns from web data. PhD thesis, University of Minnesota, USA (2000)

    Google Scholar 

  4. Huberman, B.A., Pirolli, P.L.T., Pitkow, J.E., Lukose, R.M.: Strong regularities in world wide web surfing. Science 280, 95–97 (1998)

    Article  Google Scholar 

  5. Jordan, M.I., Ghahramani, Z., Jaakola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Learning in graphical models. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  6. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: Proc. Of 9th ACM-SIAM Symposium on Discrete Algorithms (1998)

    Google Scholar 

  7. Levene, M., Loizou, G.: Computing the entropy of user navigation in the web. Technical report, Department of Computer Science, University College London (1999)

    Google Scholar 

  8. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–285 (1989)

    Article  Google Scholar 

  9. Ramoni, M., Sebastiani, P., Cohen, P.: Bayesian clustering by dynamics. Machine learning, 91–121 (2002)

    Google Scholar 

  10. Sarukkai, R.R.: Link prediction and path analysis using markov chains. In: Proceedings of the Ninth International World Wide Web Conference, Amsterdam (2000)

    Google Scholar 

  11. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)

    Article  Google Scholar 

  12. Smyth, P.: Clustering sequences with hidden markov models. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in NIPS 9 (1997)

    Google Scholar 

  13. Smyth, P.: Probabilistic model-based clustering of multivariate and sequential data. In: Proc. of 7th Int. Workshop AI and Statistics, pp. 299–304 (1999)

    Google Scholar 

  14. 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) (2000)

    Google Scholar 

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Authors and Affiliations

  1. SNN, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Alexander Ypma & Tom Heskes

Authors
  1. Alexander Ypma
  2. Tom Heskes

Editor information

Editors and Affiliations

  1. University of Alberta, Canada

    Osmar R. Zaïane

  2. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  3. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

  4. Data Miners Inc., 77 North Washington Street, MA 02114, Boston, USA

    Brij Masand

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© 2003 Springer-Verlag Berlin Heidelberg

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Ypma, A., Heskes, T. (2003). Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds) WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. WebKDD 2002. Lecture Notes in Computer Science(), vol 2703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39663-5_3

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