Abstract
As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services and motivates long term research interests in the web community. Most of the existing approaches cluster web users based on the snapshots of web usage data, although web usage data are evolutionary in the nature. Consequently, the usefulness of the knowledge discovered by existing web user clustering approaches might be limited. In this paper, we address this problem by clustering web users based on the evolution of web usage data. Given a set of web users and their associated historical web usage data, we study how their usage data change over time and mine evolutionary patterns from each user’s usage history. The discovered patterns capture the characteristics of changes to a web user’s information needs. We can then cluster web users by analyzing common and similar evolutionary patterns shared by users. Web user clusters generated in this way provide novel and useful knowledge for various personalized web applications, including web advertisement and web caching.
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