Categorization of Web Users by Fuzzy Clustering

Authors:
Giovanna Castellano;Maria Alessandra Torsello
Affiliations:
Computer Science Department, University of Bari, Bari, Italy 70126;Computer Science Department, University of Bari, Bari, Italy 70126
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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Abstract

Categorization of users is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user categories by capturing similar user interests from web usage data available in log files. Usually, fuzzy clustering is based on the use of Euclidean metrics to evaluate similarity between user preferences. This can lead to user categories that do not capture the semantic information incorporated in the original Web usage data. To better capture similarity between users, in this paper we propose the use of a measure that is based on the evaluation of similarity between fuzzy sets. The proposed fuzzy measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive categories modeling the preferences of similar users. An application example on usage data extracted from log files of a real Web site is reported and a comparison with the results obtained using the cosine measure is shown to demonstrate the effectiveness of the fuzzy similarity measure.