Model-Based Clustering and Visualization of Navigation Patterns on a Web Site View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-10

AUTHORS

Igor Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White

ABSTRACT

We present a new methodology for exploring and analyzing navigation patterns on a web site. The patterns that can be analyzed consist of sequences of URL categories traversed by users. In our approach, we first partition site users into clusters such that 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. The runtime of our algorithm scales linearly with the number of clusters and with the size of the data; and our implementation easily handles hundreds of thousands of user sessions in memory. In the paper, we describe the details of our method and a visualization tool based on it called WebCANVAS. We illustrate the use of our approach on user-traffic data from msnbc.com. More... »

PAGES

399-424

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1024992613384

DOI

http://dx.doi.org/10.1023/a:1024992613384

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018338101


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