| Web usage mining has been much concentrated on the discovery of relevant user behaviours from Web access record data. Although the sequential pattern mining has been well adapted for discovering frequent user behaviours, however, the decision makers will be more and more interested in the unexpected behaviours that contradict existing knowledge of user navigation data. In this paper, we present WebUser, an approach to discover unexpected usage in Web access log. We first formalize Web access log file into user session sequence database, with which we propose different forms of sequence rules for describing Web usage behaviours. We then present a belief-driven method for extracting unexpected Web usage sequences, where the belief system consists of a temporal relation and semantics constrained sequence rules acquired with respect to prior knowledge. Our experiments show the effectiveness and usefulness of the proposed approach. Further, discovered rules of unexpected Web usage can be used for Web content personalization and recommendation, site structure optimization, and critical event prediction. |
Récupérer au format
