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Characterizing Sequences of User Actions for Access Logs Analysis

  • Thomas Draier
  • Patrick Gallinari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

Abstract

The paper presents new measures for characterizing sequences of user actions. They are aimed at categorizing user behavior on intranet sites. Their relevance is evaluated using different encoding and clustering algorithms. New criteria are introduced for comparing clustering methods.

Keywords

Mutual Information User Action Cluster Distribution Exponential Regression Model Base Similarity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Catledge L., Pitkow J.E.: Characterizing Browsing Strategies in the world wide web, Comp. Net. and ISDN systems, 27, 1995.Google Scholar
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    The comprehensive r archive network, http://www.//r-project.org
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    Cheeseman P., Kelly J., Self M., Stutz J., Taylor W., Freeman D., Autoclass: a Bayesian classification system, Fifth ICML. Morgan Kaufmann, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Thomas Draier
    • 1
  • Patrick Gallinari
    • 1
  1. 1.LIP6, Université Paris 6ScottParis

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