Cluster optimization for enhanced web usage mining using fuzzy logic

Web Usage Mining is the process of extracting useful usage patterns from web data. Web personalization uses web usage mining technique for the process of knowledge acquisition done by analyzing the user navigational patterns. The major requirement of online industry is to fulfill the individual requirements of the user. The web page personalization involves clustering of different web pages having similar usage patterns. Clustering is an unsupervised classification of the data items into groups called clusters. There are numerous clustering algorithms based on different techniques. Most of these algorithms have some drawbacks. As the size of cluster increases due to the increase in web users, it will become inevitable need to optimize the clusters. This paper proposes a cluster optimization methodology based on fuzzy logic and is used for eliminating the redundancies occur in data after clustering done by web usage mining methods. For clustering Fuzzy C-Means (FCM) algorithm is used. Fuzzy Cluster-chase algorithm for cluster optimization is presented to personalize web page clusters of end users.