Mining Unexpected Web Usage Behaviors

Authors:
Dong (Haoyuan) Li;Anne Laurent;Pascal Poncelet
Affiliations:
LGI2P, École des Mines d'Alès, Parc Scientifique G. Besse, Númes, France 30035;LIRMM, Université Montpellier II, Montpellier, France 34392;LGI2P, École des Mines d'Alès, Parc Scientifique G. Besse, Númes, France 30035
Venue:
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Year:
2008

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Abstract

Recently, the applications of Web 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.