  Web Usage Mining is the application of data mining techniques to web usage
log repositories in order to discover the usage patterns that can be used to
analyze the users navigational behavior. During the preprocessing stage, raw
web log data is transformed into a set of user profiles. Each user profile
captures a set of URLs representing a user session. Clustering can be applied
to this sessionized data in order to capture similar interests and trends among
users navigational patterns. Since the sessionized data may contain thousands
of user sessions and each user session may consist of hundreds of URL accesses,
dimensionality reduction is achieved by eliminating the low support URLs. Very
small sessions are also removed in order to filter out the noise from the data.
But direct elimination of low support URLs and small sized sessions may results
in loss of a significant amount of information especially when the count of low
support URLs and small sessions is large. We propose a fuzzy solution to deal
with this problem by assigning weights to URLs and user sessions based on a
fuzzy membership function. After assigning the weights we apply a Fuzzy c-Mean
Clustering algorithm to discover the clusters of user profiles. In this paper,
we describe our fuzzy set theoretic approach to perform feature selection (or
dimensionality reduction) and session weight assignment. Finally we compare our
soft computing based approach of dimensionality reduction with the traditional
approach of direct elimination of small sessions and low support count URLs.
Our results show that fuzzy feature evaluation and dimensionality reduction
results in better performance and validity indices for the discovered clusters.
