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Taking the temporal dimension into account during the analysis of Web usage data has become a necessity since the way a site is visited may well 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. One solution to this problem is to update these models using summaries obtained by means of an evolutionary approach based on clustering methods. To do this, we carry out various clustering strategies that are applied on time sub-periods. We compare the results obtained using this method with those reached by traditional global analysis.