Web Log Mining Based on Soft Clustering and Multi-Objective Genetic Algorithm

Web mining can be broadly defined as discovery and analysis of useful information from the World Wide Web. Web Usage Mining can be described as the discovery and analysis of user accessibility pattern, during the mining of log files and associated data from a particular Web site, in order to realize and better serve the needs of Web-based applications. Web log mining is important area of research for the improvement of web efficiency and log cache enhancement. web log mining various method of data mining is applied one such method is called clustering. Clustering is unsupervised learning technique of data mining. The form of this clustering is k-means and k-median algorithm, but these algorithm are suffered some point of problem now used soft clustering technique such as fuzzy clustering algorithm. Web mining are classify into three domains: content, structure and usage mining. The purpose of this paper is to provide optimum initial solution for FCM with the help of genetic algorithm For the improvement of FCM clustering technique used multi-objective genetic algorithm for better generation of clustering technique. In this paper discuss FCM algorithm, multi-objective genetic algorithm.