Analysing User Navigation Patterns through Association Rule Mining and Clustering: A Case Study on Web Server Logs

Abstract

Web usage mining improves the efficiency and personalization of Web-based services by using data mining techniques to identify patterns in user activity from Web log data. Preprocessing, pattern finding, and pattern analysis are its three primary stages. Among other methods, association rule mining is essential for identifying connections between user actions or viewed web sites. In addition to offering a thorough taxonomy of current research and commercial systems in the field, this study investigates the application of association rule mining within the larger framework of Web usage mining. This study examines user navigation behavior by analysing web server logs. Using association rule mining and clustering techniques, we identify frequent usage patterns and group similar webpages. Key findings highlight connections between webpage visits and offer insights into user behaviour over time.