Web Sessions Clustering with Artificial Ants Colonies

In this paper, we present AntClust, an ant based clustering algorithm and its application to the Web usage mining problem. We define a Web session as a weighted multi-modal vector and we also develop a similarity measure between two sessions. We show that the partitions found by AntClust are stable on a data set made of real sessions extracted from a Web site of the University of Tours. Contrary to some other studies, we do not only consider the transactions model to describe the sessions. We show that our algorithm performs well and is able to find non-noisy clusters when dealing with sessions defined by a vector containing the number of hits recorded for each of the Web page.