Web Usage Mining for Comparing User Access Behavior using Clustering

Amit Dipchandji Kasliwal, Dr. Girish S. Katkar

Abstract: Motivated by the practical needs in improving the access to information over the internet with comparison between the users, many authors have given their most time on studying and designing the framework for the application of data mining like web mining, text mining and so on. Now it is the time to study data mining in systematic manner with few foundational measures related to the implementation of web mining through proposed low level prototype WebUMining. In this study, authors worked on data mining techniques applied successfully for suggesting the web usage mining to the organizational and authority to improve their website by comparing the user access and access behavior with result discussed and analysis. Authors used datasets from reliable repositories, which applied with algorithm proposed in WebUMining model. These datasets treated as training data for the all proposed algorithms. This study of data mining has primarily focused on the mining algorithms which are used in web usage mining.

Keywords: Web Usage Mining, User Access Behavior, Clustering, Web Access Log Data, WEKA