Web Users Clustering Based on Fuzzy C-MEANS

Abstract

The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means. In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.