Generating a New Model for Predicting the Next Accessed Web Page in Web Usage Mining

World Wide Web is growing rapidly. So it is necessary to study the user web navigation behavior to improve the quality of web services, offered to the web user. Analysis of user web navigation behavior is achieved through modeling web navigation history. Markov model is widely used to model the user web navigation sessions. Lower-order Markov model provides high coverage, but with low accuracy. Higher-order Markov model give low coverage but high accuracy with more time complexity. In this paper a new way of structuring the Markov model is proposed named as Dynamic Nested Markov model for modeling the user web navigation sessions. Dynamic Nested Markov model uses the nesting concept, the higher-order Markov model is nested inside the lower-order Markov model. Through this nesting, the second-order Markov model is accommodated inside the first-order Markov model. In Dynamic Nested Markov model, all the advantages of lower-order model and higher-order model are achieved in one model. In this model focus is on time complexity and coverage of the prediction state. Result shows that the high coverage has achieved and time complexity has been reduced.