WEB PAGES RECOMMENDATION SYSTEM BASED ON K-MEDOID CLUSTERING METHOD

Abstract

 With an expontial growth of World Wide Web, there are so many information overloaded and it become hard
to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of
user navigation pattern from web log. Web Recommendation System is implemented by using Collaborative Filtering
approach. It is a specific type of information filtering system that aims to predict the user browsing activity and then
recommended to the user web pages items that are likely to be of interest. In this paper, a new recommendation system is
proposed by using K- Medoid clustering approach to predict the user’s navigational behavior. The proposed
recommendation system based on K-medoid clustering performs well compared to K-Mean clustering algorithm. The
performance of the comparative analysis is presented through given experimental results.