A Dynamic Clustering-Based Markov Model for Web Usage Mining
<|reference_start|>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 effcient 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.<|reference_end|>
@article{borges2004a,
    title={A Dynamic Clustering-Based Markov Model for Web Usage Mining},
    author={Jos'e Borges (1), Mark Levene (2) ((1) School of Engineering,
  University of Porto, Portuga,(2)Birkbeck, University of London, U.K)},
    journal={arXiv preprint arXiv:cs/0406032},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0406032},
    primaryClass={cs.IR cs.AI}
}