id: 05487537
dt: a
an: 05487537
au: Castellano, Giovanna; Torsello, Maria Alessandra
ti: Categorization of web users by fuzzy clustering.
so: Lovrek, Ignac (ed.) et al., Knowledge-based intelligent information and
    engineering systems. 12th international conference, KES 2008, Zagreb,
    Croatia, September 3‒5, 2008. Proceedings, Part II. Berlin: Springer
    (ISBN 978-3-540-85564-4/pbk). Lecture Notes in Computer Science 5178.
    Lecture Notes in Artificial Intelligence, 222-229 (2008).
py: 2008
pu: Berlin: Springer
la: EN
cc: 
ut: Web mining; Fuzzy clustering; access log; Web personalization; user
    profiling
ci: 
li: doi:10.1007/978-3-540-85565-1_28
ab: Summary: 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.
rv: