Abstract
Web Usage Mining (WUM) is the process of extracting knowledge from Web user behavior on web, who are actively involved in accessing the web data by exploiting Data Mining techniques. This knowledge can be used for various purposes such as personalization, system development and site improvement. This knowledge discovery is also useful for web designer to quickly respond to the web user needs. The researcher has developed no of classification technique to extract the user log data to find the interested web user. This paper reviews the various existing classification techniques for web user data mining and presents a comparative analysis of these classification techniques.
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