VISITOR NAVIGATION PATTERN PREDICTION USING MARKOV MODELS, ASSOCIATION RULES AND AMBIGUOUS RULES

Abstract

There are large number of Web sites which consist of many web pages. It is more difficult for the users to quickly get their target pages. The main aim of this paper is to only implement Visitor Navigation Pattern Prediction Using Markov models, Association Rules and Ambiguous Rules. The paper uses traveling paths that assist visitors to navigate the visiting places based on the past visitor’s behavior.  In this article, Markov models and association rules and ambiguous rules were used to resolve ambiguous web access predictions .An improved method that organizes Markov models, association rules and ambiguous rules and combines web pages into a web site for prediction. This method can offer better predictions than using each method alone and other traditional models. It uses web usage mining techniques for recommending a visitor which (next)paths is closely the most popular paths in Myanmar.