A Dynamic Clustering-Based Markov Model for Web Usage Mining
| dc.creator | Borges, José | |
| dc.creator | Levene, Mark | |
| dc.date | 2004-06-17 | |
| dc.date.accessioned | 2026-07-07T03:21:27Z | |
| dc.date.available | 2026-07-07T03:21:27Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/cs/0406032 | |
| dc.identifier | http://arxiv.org/abs/cs/0406032 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32202 | |
| dc.subject | Information Retrieval | |
| dc.subject | Artificial Intelligence | |
| dc.title | A Dynamic Clustering-Based Markov Model for Web Usage Mining | |
| dc.type | text |