A Dynamic Clustering-Based Markov Model for Web Usage Mining

TL;DR

This paper introduces a dynamic clustering-based Markov model that improves web usage prediction accuracy by intelligently cloning states based on second-order probability divergence, demonstrated through experiments with real and synthetic data.

Contribution

The paper presents a novel dynamic clustering approach with state cloning for Markov models, enhancing their ability to model user web navigation behavior.

Findings

The method controls the number of states via a threshold parameter.

Performance scales linearly with model size.

Outperforms traditional N-gram Markov models.

Abstract

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.