Аннотация
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.
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