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Data Mining of User Navigation Patterns

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Web Usage Analysis and User Profiling (WebKDD 1999)

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

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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.

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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
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  2. Mark Levene
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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

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© 2000 Springer-Verlag Berlin Heidelberg

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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

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67818-2

  • Online ISBN: 978-3-540-44934-8

  • eBook Packages: Springer Book Archive

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