Web Access Path Prediction Using Fuzzy Case Based Reasoning

In this paper, a fuzzy case based reasoning approach to Web access path prediction is developed and tested. It is based on the assumption that new user's access patterns can be predicted by referencing to the behaviors of similar Web users in the past. This method has three phases. Firstly, a Web case base is constructed from the Web log data. This includes the pre-processing and cleaning of the Web log data so that a suitable format is developed. Secondly, contextual information is extracted from the Web pages, and this information is used to develop a similarity measurement between Web pages. This information is added to the Web case base. Finally, fuzzy association rule mining is used to discover the relationship between the browsing behavior (user navigations) and the Web contents using the Web case base. A set of predictive cases from the Web case base is then selected for the access path prediction. From the experimental evaluation, our approach has demonstrated better prediction accuracy than the existing approaches.