Clustering Web Sessions Using Extended General Pages.
Zhongming Ma, Olivia R. Liu Sheng · Journal of the Association for Information Systems · 2004
We study Web sessions clustering in order to find groups of similar sessions and discover user access patterns on a Web site. We extend the general page concept presented in (Fu, Sandhu and Shih 2000) by including partial document names and dynamic pages, and use an extended general page (EGP) to represent many individual page URLs sharing the same EGP. We present two extensions of a hierarchical clustering algorithm, ROCK (Guha, Rastogi and Shim 2000). One is a notion of EGP count that we add to the session similarity calculation. The other is a goodness threshold we adopt to restrict certain clusters from merging with others. Further, we propose a set of measurements for assessing the results from clustering boolean and categorical data and help users to identify their desired clustering results. In our experiments, we applied the ROCK and the extended ROCK (EROCK) algorithms to cluster a half-month’s Web log from a customer service Web site at HP. The experiment results showed that E-ROCK alleviated a large cluster problem of the ROCK algorithm and improved the performance in intra cluster similarity.