WEB PAGES RECOMMENDATION SYSTEM BASED ON K-MEDOID CLUSTERING METHOD
| Author(s) | : | Richa Patel, Akshay Kansara |
| Institution | : | P.G. Student |
| Published In | : | Vol. 2, Issue 5 — May 2015 |
| Page No. | : | 745-751 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
With an expontial growth of World Wide Web, there are so many information overloaded and it become hardto find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery ofuser navigation pattern from web log. Web Recommendation System is implemented by using Collaborative Filteringapproach. It is a specific type of information filtering system that aims to predict the user browsing activity and thenrecommended to the user web pages items that are likely to be of interest. In this paper, a new recommendation system isproposed by using K- Medoid clustering approach to predict the user’s navigational behavior. The proposedrecommendation system based on K-medoid clustering performs well compared to K-Mean clustering algorithm. Theperformance of the comparative analysis is presented through given experimental results.
Richa Patel, Akshay Kansara, “WEB PAGES RECOMMENDATION SYSTEM BASED ON K-MEDOID CLUSTERING METHOD”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 5, pp. 745-751, May 2015.
