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In this paper, we present a system based on an Unsupervised Fuzzy Divisive Hierarchical Clustering (UFDHC) algorithm to determine a hierarchy of profiles of web site typical users from the web access log. These profiles can be extremely useful, for instance, to customize the web site, or to send personalized advertisements. After eliminating categories that have not been accessed by a significant percentage of users and removing the occasional users, the access log data are input to the UFDHC algorithm which clusters the users of the web site into a hierarchy of groups characterized by a set of common interests and represented by a prototype, which defines the profile of the group typical member. To show the effectiveness of our system, we describe how the profiles determined by the UFDHC algorithm from access log data collected along a period of 15 days allow classifying approximately 95% of the users defined by access log data collected during subsequent 60 days.