Web Personalization Using Clustering of Web Usage Data

The exponential growth in the number and the complexity of information resources and services on the Web has made log data an indispensable resource to characterize the users for Web-based environment. It creates information of related web data in the form of hierarchy structure through approximation. This hierarchy structure can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc. In this paper, we present an approach for personalizing web user environment dynamically when he interacting with web by clustering of web usage data using concept hierarchy. The system is inferred from the web server’s access logs by means of data and web usage mining techniques to extract the information about users. The extracted knowledge is used for the purpose of offering a personalized view of the services to users.