A Comparative Study on Latest Substring Association Rule Mining and Hidden Markov Model

Abstract

The Web usage mining techniques are used to scrutinize the web usage patterns for a web site. Web page prediction plays a vital role by predicting next set of web pages that a user may visit based on the knowledge of the previously visited pages. Web page prediction is the focus of attention of many researchers in recent times and different web page prediction frameworks have been proposed. In this paper, a comparative analysis between two different approaches of web page prediction, namely, Latest Substring Association Rule mining (LSA) and Hidden Markov Model (HMM) has been represented. Web page prediction is implemented by using both the approaches and the experimental results are provided. Finally, an improved approach for web page prediction is proposed at the end of the paper.