Performance Analysis Of Web Page Prediction With Markov Model, Association Rule Mining(Arm) And Association Rule Mining With Statistical Features(Arm-Sf)

Abstract

Web prediction is a classification problem in which we have to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior can be applied effectively in various critical applications in the internet environment.Such application has traditional tradeoffs between modeling complexity and prediction accuracy. The web usage mining techniques are used to analyze the web usage patterns for a web site. The user access log is used to fetch the user access patterns. The access patterns are used in the prediction process. Markov model and all-K th Markov model are used in Web prediction. A Markov model is proposed to alleviate the issue of scalability in the number of paths. The framework can improve the prediction time without compromising prediction accuracy. The proposed system is to compare the prediction accuracy with the markov model, ARM, ARM-SF and Boosting and Bagging model. The system improves the accuracy with scalability considerations. Finally the result will shows which would have better prediction accuracy. Keywords: Association rule mining (ARM),Association rule mining with statistical features(ARM-SF),Markov model.