Hybrid technique for user's web page access prediction based on Markov model

Abstract

Web Mining consists of three different categories, namely Web Content Mining, Web Structure Mining, and Web Usage Mining (is the process of discovering knowledge from the interaction generated by the users in the form of access logs, browser logs, proxy-server logs, user session data, cookies). This paper present mining process of web server log files in order to extract usage patterns to web link prediction with the help of proposed Markov Model. The approaches result in prediction of popular web page or stage and user navigation behavior. Proposed technique cluster user navigation based on their pair-wise similarity measure combined with markov model with the concept of apriori algorithm which is used for Web link prediction is the process to predict the Web pages to be visited by a user based on the Web pages previously visited by other user. So that Web pre-fetching techniques reduces the web latency & they predict the web object to be pre-fetched with high accuracy and good scalability also help to achieve better predictive accuracy among different log file The evolutionary approach helps to train the model to make predictions commensurate to current web browsing patterns.