Analysis of Web server logs is one of the important challenge to provide Web intelligent services.In this paper, we describe a framework for a recommender system that predicts the user's next requests based on their behaviour discovered from Web Logs data. We compare results from three usage mining approaches: association rules, sequential rules and generalised sequential rules. We use two selection rules criteria: highest confidence and lastsubsequence. Experiments are performed on three collections of real usage data: one from an Intranet Web site and two from an Internet Web site.

Abstract. Modularity allows to estimate the quality of a partition into communities of a graph composed of highly inter-connected vertices. In this article, we introduce a complementary measure, based on inertia, and specially conceived to evaluate the quality of a partition based on real attributes describing the vertices. We propose also I-Louvain, a graph nodes clustering method which uses our criterion, combined with Newman's modularity, in order to detect communities in attributed graph where real attributes are associated with the vertices. Our experiments show that combining the relational information with the attributes allows to detect the communities more efficiently than using only one type of information. In addition, our method is more robust to data degradation.