An Effective Web User Analysis and Clustering using FPCM Algorithm

Internet is one of the users friendly to the world and also gives lot of information for every activity. Based on this there is huge amount of development in World Wide Web. There are varieties of issues connected with the existing web usage mining approaches. Applying data mining techniques to the discovery of usage patterns from Web data and various applications is the process for web usage mining. Practical applicability is one of the problems in web usage mining. The essence of accurate fast time routine of web user information systems to the needs of their user. Preprocessing and clustering of web user are important evolve in this paper. Preprocessing is one of the key processes to remove noisy log data and to produce a clear data as an effective mining process. Examine a novel approach to removing local and global noise and web robots and by preprocessing, clustering Web site users into different groups and generating common user profiles. This type of profile is useful for the user to develop their own personalize website and for various applications. FPCM (Fuzzy Possibilistic C Means) is one of the algorithms to use easily. FPCM gives comparable results with FCM (Fuzzy C Means) reported in the literature of web mining. Anonymous Microsoft Web Dataset is used for evaluating the proposed preprocessing technique and clustering process.

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