Hybrid connectionist approach for knowledge discovery from Web navigation patterns

Summary form only given. We apply the "EM" algorithm to learn the parameters of a Markov chain mixture model for clustering navigation sessions on a Web site. Our main contribution is to deduce the model's initial parameters from clusters formed by a hierarchical clustering of a sample of sessions, whose dissimilarity matrix is computed by dynamic time warping. The states of the Markov chains are the neurons of a Kohonen self organizing map, which displays the site as it is seen by the users and also clusters its pages (one neuron corresponding to a cluster of pages). This technique for clustering sessions has been validated on a set of semiartificial data and the results are excellent. Finally, we tested several criteria for the determination of the optimal number of clusters and concluded that the Akaike information criterion was best suited to this problem.