The World Wide Web, also called the Web, behaves as an information space where documents, web pages, graphics, audio, video files and other widespread web resources are identified and accessible at real time. Due to vast and varied information on the web, the web users cannot access the relevant information very effectively and easily. A web user spends a lot of time over the Internet. For understating web users’ interest area, it is necessary to analyse the surfing pattern of user’s internet access. Web usage mining is a tool to discover and perform analysis of interesting web usage patterns from web log data. The methodology requires to identify the usage from the web proxy log files. It also includes techniques for Noise Removal from log files; determine the Client, determine the Client Session, Access Path Enhancement, determine the Transaction, Path investigation, and association rule investigation, Consecutive Pattern, Fuzzy Clustering and Fuzzy Classification. For imprecise, vague and uncertainty in data items we must use fuzzy approach. Fuzzy C-Means (FCM) is an unsupervised clustering algorithm based on fuzzy approach that permits an element to belong to more than one cluster. Here fuzzy means “unclear” or “not defined” and C denotes “clustering”. In this paper, we have reviewed and discussed latest Web Usage Mining Fuzzy Cluster techniques, issues and challenges.

Web Usage Mining, Cluster, Fuzzy Set, Cluster, K-Means, FCM, FPCM, MFPCM, EMFPCM, KFCM, YKFCM, KFCM

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