COWES: Clustering Web Users Based on Historical Web Sessions

Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by clustering web users based on the evolutions of their historical web sessions. We present an algorithm called COWES to cluster web users in three steps. First, given a set of web users, we mine the history of their web sessions to extract interesting patterns that capture the characteristics of their usage data evolution. Then, the similarity between web users is computed based on their common interesting patterns. Then, the desired clusters are generated by a partitioning clustering technique. Web user clusters generated based on their historical web sessions are useful in intelligent web advertisement and web caching.