Abstract
Determining profiles of web portal typical users can be extremely useful, for instance, to personalize the web portal, to provide customized guide and to send tailored advertisements. In this work, we present a system to produce a small number of user profiles from the web access log and to associate each user with one of these profiles. The system is based on a version of the fuzzy C-means (FCM) algorithm which uses the cosine distance rather than the classical Euclidean distance. After filtering the access log, for instance, by removing occasional and undecided users, the FCM algorithm clusters the users into groups characterized by a set of common interests and represented by a prototype, which defines the profile of the group typical member. To attest the validity of these profiles, we extract a set of association rules from the raw access log data by applying the well-known A-priori algorithm and show how the profiles are a concise representation of the association rules. Finally, to test the effectiveness of the overall fuzzy system, we illustrate how the profiles determined by the FCM algorithm from access log data collected along a period of 30 days allow classifying approximately 93% of the users defined by access log data collected during subsequent 30 days.