Visualization of navigation patterns on a Web site using model-based clustering

Abstract

We present a new methodology for visualizing navigation patterns on a Web site. In our approach, we first partition site users into clusters such that only users with similar navigation paths through the site are placed into the same cluster. Then, for each cluster, we display these paths for users within that cluster. The clustering approach we employ is model based (as opposed to distance based) and partitions users according to the order in which they request Web pages. In particular, we cluster users by learning a mixture of first-order Markov models using the Expectation-Maximization algorithm. Our algorithm scales linearly with both number of users and number of clusters, and our implementation easily handles millions of users and thousands of clusters. In the paper, we describe the details of our technology and a tool based on it called WebCANVAS. We illustrate the use of our technology on user-traffic data from msnbc.com.