Web Usage Data Clustering Using Improved Genetic Fuzzy C-Means Algorithm

Web usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. Recent attempts have adapted the C-means clustering algorithm as well as genetic algorithms to find sets of clusters .In this paper; we have proposed a new framework to improve the web sessions’ cluster quality from fuzzy c-means clustering using Improved Genetic Algorithm (GA). Initially a fuzzy c-means algorithm is used to cluster the user sessions. The refined initial starting condition allows the iterative algorithm to converge to a “better” local minimum. And in the second step, we have proposed a new GA based refinement algorithm to improve the cluster quality. The proposed algorithm is tested with web access logs collected from the UCI dataset repository.