Title: WebUser: mining unexpected web usage

Authors: Dong Li, Anne Laurent, Pascal Poncelet

Addresses: LGI2P, Ecole des Mines d'Ales, Parc scientifique Georges Besse, 30035 Nimes, France. ' LIRMM, Universite Montpellier 2, 161 rue Ada, 34392 Montpellier, France. ' LIRMM, Universite Montpellier 2, 161 rue Ada, 34392 Montpellier, France

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. Furthermore, 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; web access logs; belief systems; temporal relations; semantics; prior knowledge; web content personalisation; recommendations; site structure optimisation; critical event prediction.

DOI: 10.1504/IJBIDM.2011.038276

International Journal of Business Intelligence and Data Mining, 2011 Vol.6 No.1, pp.90 - 111

Received: 24 Feb 2009
Accepted: 25 Jun 2009
Published online: 22 Apr 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article