Merging Data Mining Techniques for Web Page Access Prediction: Integrating Markov Model with Clustering
Trilok Nath Pandey, Ranjita Kumari Dash, Alaka Nanda Tripathy, Barnali Sahu · 2012
Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalizing the Web users’ browsing experiences, assists Web masters in the improvement of the Website structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page to be accessed by the Web user based on the user’s previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper achieves better accuracy as well as less state space complexity and rules generated by performing the following combinations. We integrate low -order Markov model and clustering. The data sets are clustered and Markov model analysis is performed on each cluster instead of the whole data sets. The outcome of the integration is better accuracy than the combination with less state space complexity than higher order