Discovery of Web Usage Profiles Using Various Clustering Techniques
This work addresses the need for web personalization systems to understand user preferences, but it is incremental as it applies existing methods to web data without introducing new techniques.
The paper reviews and compares four popular clustering techniques (k-Means, k-Medoids, Leader, and DBSCAN) applied to web usage data to discover user profiles, presenting performance and validity results for each.
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.