Abstract
Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.
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