Predicting next page access by Markov models and association rules on web log data

Mining user patterns of log file can provide significant and useful informative knowledge. A large amount of the research has been concentrated on trying to correctly predict the pages a user will request. This task requires the development of models that can predict a user’s next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site based on past visitor behavior compare with association rules technique. This algorithm has been used to cluster Web site with similar transition behaviors and compares the transition matrix to an optimal size for efficient used to further improve the efficiency of prediction. From this comparison we propose a best overall method and empirically test the proposed model on real web logs

Hybrid web page prediction model for predicting a user's next access

The web user sessions are clustered with incorporating the sequence of web page visits. A sequence-based clustering is developed by proposing new sequence representations and new similarity measures. The resulting sequence representation allows for calculation of similarity between web user sessions and then, can be used as input of clustering algorithms. This study proposed a hybrid prediction model (HyMFM) that integrates Markov model, Association rules and Fuzzy Adaptive Resonance Theory (Fuzzy ART) clustering together. The three approaches are integrated to maximize their strengths. A series of experiments was conducted to investigate whether, clustering performance is affected by different sequence representations and different similarity measures. This model could provide better prediction than using each approach individually