Clustering of Web Users Based on Access Patterns

The clustering of the Web users based on their access patterns is studied. Access patterns of the Web users are extracted from Web servers' log les, and then organized into sessions which represent episodes of interaction between Web users and the Web server. Using attributed-oriented induction, the sessions are then generalized according to the page hierarchy which organizes pages according to their generalities. The generalized sessions are nally clustered using a hierarchical clustering method. Our experiments on a large real data set show that the method is eecient and practical for Web mining applications.