An Application of Session Based Clustering to Analyze Web Pages of User Interest from Web Log Files

Abstract

Problem statement: With the continued growth and proliferation of e-commerce, Web services and Web-based information systems, the volumes of click-stream and user data collected by Web-based organizations in their daily operations have reached astronomical proportions. Analyzing such data can help these organizations optimize the functionality of web-based applications and provide more personalized content to visitors. This type of analysis involved the automatic discovery of usage interest on the web pages which are often stored in web and applications server access logs. Approach: The usage interest on the web pages in various sessions was partitioned into clusters such that sessions with “similar” interest were placed in the same cluster using expectation maximization clustering technique as discussed in this study. Results: The approach results in the generation of usage profiles and automatic identification of user interest in each profile. Conclusion: The significance of the results will be helpful for organizations for web site improvement based on their navigational interest and provide recommendations for page(s) not yet visited by the user.