Mining Unexpected Web Usage Behaviors

Abstract : Recently, the applications ofWeb usage mining are more and more concentrated on finding valuable user behaviors from Web navigation record data, where the sequential pattern model has been well adapted. However with the growth of the explored user behaviors, the decision makers will be more and more interested in unexpected behaviors, but not only in those already confirmed. In this paper, we present our approach USER, that finds unexpected sequences and implication rules from sequential data with user defined beliefs, for mining unexpected behaviors from Web access logs. Our experiments with the belief bases constructed from explored user behaviors show that our approach is useful to extract unexpected behaviors for improving the Web site structures and user experiences.
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Communication dans un congrès
ICDM'08: 8th Industrial Conference on Data Mining, pp.283-297, 2008, 〈http://www.data-mining-forum.de/〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00275952
Contributeur : Haoyuan Li <>
Soumis le : mardi 24 mars 2009 - 11:04:02
Dernière modification le : jeudi 11 octobre 2018 - 10:28:04
Document(s) archivé(s) le : vendredi 28 mai 2010 - 17:48:02

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Haoyuan Li, Anne Laurent, Pascal Poncelet. Mining Unexpected Web Usage Behaviors. ICDM'08: 8th Industrial Conference on Data Mining, pp.283-297, 2008, 〈http://www.data-mining-forum.de/〉. 〈lirmm-00275952〉

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