A Dynamic Clustering-Based Markov Model for Web Usage Mining

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A Dynamic Clustering-Based Markov Model for Web Usage Mining
Markov models have been widely utilized for modelling user web navigation behaviour. In this work we propose a dynamic clustering-based method to increase a Markov model's accuracy in representing a collection of user web navigation sessions. The method makes use of the state cloning concept to duplicate states in a way that separates in-links whose corresponding second-order probabilities diverge. In addition, the new method incorporates a clustering technique which determines an efficient way to assign in-links with similar second-order probabilities to the same clone. We report on experiments conducted with both real and random data and we provide a comparison with the N-gram Markov concept. The results show that the number of additional states induced by the dynamic clustering method can be controlled through a threshold parameter, and suggest that the method's performance is linear time in the size of the model.
José Borges, Mark Levene
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CORR
Authors José Borges, Mark Levene
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