IEEE TRANSACTIONS ON SYSTEMS AUGUST 2012
Technology - Available in JAVA /J2EE
Abstract— Web prediction is a classification problem in
which we attempt 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 while serving the Internet can be applied effectively in various
critical applications. Such application has traditional tradeoffs between
modeling complexity and prediction accuracy. In this paper, we analyze and
study Markov model and all-Kth Markov model in Web prediction. We
propose a new modified Markov model to alleviate the issue of scalability in
the number of paths. In addition, we present a new two-tier prediction
framework that creates an example classifier EC, based on the
training examples and the generated classifiers. We show that such framework
can improve the prediction time without compromising Prediction accuracy.
We have used standard benchmark data sets to analyze, compare, and demonstrate
the effectiveness of our techniques using variations of Markov models and
association rule mining. Our experiments show the effectiveness of our modified
Markov model in reducing the number of paths without compromising accuracy.
Additionally, the results support our analysis conclusions that accuracy improves
with higher orders of all-Kth model
