  The explosive growth of World Wide Web (WWW) has necessitated the development
of Web personalization systems in order to understand the user preferences to
dynamically serve customized content to individual users. To reveal information
about user preferences from Web usage data, Web Usage Mining (WUM) techniques
are extensively being applied to the Web log data. Clustering techniques are
widely used in WUM to capture similar interests and trends among users
accessing a Web site. Clustering aims to divide a data set into groups or
clusters where inter-cluster similarities are minimized while the intra cluster
similarities are maximized. This paper reviews four of the popularly used
clustering techniques: k-Means, k-Medoids, Leader and DBSCAN. These techniques
are implemented and tested against the Web user navigational data. Performance
and validity results of each technique are presented and compared.
