Effective Web Usage Mining by Tracing Visitors Online Behaviors

Web mining can be defined as the extraction and analysis of useful information from the Web. It can also be defined as automatic discovery of patterns in click streams and associated data collected or generated as a result of user interactions with one or more Web sites. User behavior identification is an important task in web usage mining. Web usage mining is also called as web log mining. The web logs are mainly used to identify the user behavior. There are so many pattern mining methods which enable this user behavior identification. The preprocessing techniques will maximize the accurate and quality of pattern mining methodologies. In existing algorithms, the preprocessing concepts are applied to calculate the unique users count, to minimize the log file size and to identify the sessions. The newly proposed algorithm is Visitors Online Behavior (VOB) which identifies user behavior, creates user cluster and page cluster, and tells the Highest popular web page and least popular web page. This paper includes the discussion about the basic concepts of web mining, web usage mining, general data preprocessing, how to preprocess the web data, what are the various existing preprocessing techniques and the proposed VOB algorithm