A hierarchical cluster based preprocessing methodology for Web Usage Mining

In Web Usage Mining (WUM), web session clustering plays a key role to classify web visitors on the basis of user click history and similarity measure. Swarm based web session clustering helps in many ways to manage the web resources effectively such as web personalization, schema modification, website modification and web server performance. In this paper, we propose a framework for web session clustering at preprocessing level of web usage mining. The framework will cover the data preprocessing steps to prepare the web log data and convert the categorical web log data into numerical data. A session vector is obtained, so that appropriate similarity and swarm optimization could be applied to cluster the web log data. The hierarchical cluster based approach will enhance the existing web session techniques for more structured information about the user sessions.