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Predicting Page Occurrence in a Click-Stream Data: Statistical and Rule-Based Approach

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Advances in Data Mining. Theoretical Aspects and Applications (ICDM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4597))

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Abstract

We present an analysis of the click-stream data with the aim to predict the next page that will be visited by an user based on a history of visited pages. We present one statistical method (based on Markov models) and two rule induction methods (first based on well known set covering approach, the other base on our compositional algorithm KEX). We compare the achieved results and discuss interesting patterns that appear in the data.

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Authors and Affiliations

  1. Department of Information and Knowledge Engineering, University of Economics, W. Churchill Sq. 4, 130 67 Prague, Czech Republic

    Petr Berka & Martin Labský

Authors
  1. Petr Berka
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  2. Martin Labský
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Petra Perner

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© 2007 Springer-Verlag Berlin Heidelberg

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Berka, P., Labský, M. (2007). Predicting Page Occurrence in a Click-Stream Data: Statistical and Rule-Based Approach. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_11

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  • DOI: https://doi.org/10.1007/978-3-540-73435-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73434-5

  • Online ISBN: 978-3-540-73435-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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