Abstract: Web usage mining has been much concentrated on the discovery of relevant user behaviours from Web access record data. In this paper, we present WebUser, an approach to discover unexpected usage in Web access log. We 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 personalisation and recommendation, site structure optimisation, and critical event prediction. Keywords: data mining; web usage mining; log analysis; unexpected usage; sequence rules; concept hierarchies. Anne Laurent received her PhD Degree in Computer Science at the University of Paris 6, France. She is an Assistant Professor at the University of Montpellier 2, France. As a member of the TATOO team in the Laboratory LIRMM, she works on data mining, sequential pattern mining, tree mining, both for trends and exceptions detections and is particularly interested in the study of the use of fuzzy logic to provide more valuable results, while remaining scalable.
The discovered rules of unexpected web usage were used for web content personalisation and recommendation, site structure optimisation and critical event prediction REF .
WebUser : mining unexpected web usage
IJBIDM
IJBIDM
Computer Science