Model-based clustering and visualization of navigation patterns on a web site

Model-based clustering and visualization of navigation patterns on a web site (2003)

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

Venue:	Data Mining and Knowledge Discovery
Citations:	36 - 0 self

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Datum	Value	Source
TITLE	Model-based clustering and visualization of navigation patterns on a web site	INFERENCE
AUTHOR NAME	Igor Cadez	SVM HeaderParse 0.2
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.2
AUTHOR NAME	Christopher Meek	SVM HeaderParse 0.2
AUTHOR NAME	Padhraic Smyth	SVM HeaderParse 0.2
AUTHOR NAME	Steven White	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Information and Computer Science; University of California, Irvine; Microsoft Research; Microsoft Corporation	SVM HeaderParse 0.2
AUTHOR ADDR	; CA 92717-3425; One Microsoft Way; Redmond, WA 980521Abstract	SVM HeaderParse 0.2
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 rst 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 weemployis 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 rst-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-tra c data from msnbc.com. Keywords: Model-based clustering, sequence clustering, data visualization, Internet, web 1	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	Data Mining and Knowledge Discovery	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	399--424	INFERENCE
CITATIONS	34 found	ParsCit 1.0