Model-based cluster analysis for Web users sessions

Abstract

One of the main issues in Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of users' sessions and discovering association rules or frequent navigational paths, do not generally allow to characterize or quantify the unobservable factors that lead to common navigational patterns. Therefore, it is necessary to develop techniques that can discover hidden and useful relationships among users as well as between users and Web objects. Correspondence Analysis (CO-AN) is particularly useful in this context, since it can uncover meaningful associations among users and pages. We present a model-based cluster analysis for Web users sessions including a novel visualization and interpretation approach which is based on CO-AN. © Springer-Verlag Berlin Heidelberg 2005.