Analyzing Web usage has become a very important strategy for Web site operators as it provides them a better understanding of the users' behavior. This insight can enable the operators to improve their service and thereby attract more visitors. Taking into account the temporal dimension in such analyses has become a necessity since the way a site is visited can indeed evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. Consequently, the models associated with these behaviors must be continuously updated in order to reflect the actual behavior of the users. One solution to this problem, proposed in this article, is to update these models using summaries obtained by means of an evolutionary approach based on clustering methods. To do so, we carry out various clustering strategies that are applied on time sub-periods. We compare the results obtained using this method with the results obtained by a traditional global analysis.