Abstract:
Web user profiling targets grouping users in to
clusters with similar interests. Web sites are attracted by many
visitors and gaining insight to the patterns of access leaves lot
of information. Web server access log files record every single
request processed by web site visitors. Applying web usage mining
techniques allow to identify interesting patterns. In this paper we
have improved the similarity measure proposed by Vel´asquez et
al. [1] and used it as the distance measure in an agglomerative
hierarchical clustering for a data set from an online banking web
site. To generate profiles, frequent item set mining is applied over
the clusters. Our results show that proper visitor clustering can
be achieved with the improved similarity measure.
