International Journal of Engineering and Technology

Web usage mining performs mining on web usage data or web logs. It is now possible to perform data mining on web log records collected from the web page history. A web log is a listing of page reference data/click stream data. The behavior of the web page readers is imprinted in the web server log files. By looking at the sequence of pages a user accesses, a user profile could be developed thus aiding in personalization. With personalization, web access or the contents of web page are modified to better fit the desires of the user and also to identify the browsing behavior of the user can improve system performance, enhance the quality and delivery of Internet Information services to the end user and identify the population of potential customers. With clustering, the desires are determined based on similarities. In this study, a Fuzzy clustering algorithm is designed and implemented. For the proposed algorithm, meaningful behavior patterns are extracted by applying efficient Fuzzy clustering algorithm, to log data. It is proved that performance of the proposed system is better than that of the existing best algorithm. The proposed Fuzzy clustering w-miner algorithm can provide popular information to web page visitors.