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 Vela?squez 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.
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