Abstract

The arrival of the computer technology has contributed the ability to produce and store the massive amounts of data. Now the world is not confined only to manually generated files or reports, but has become a giant store where vast amounts of data are collected and exchanged daily. Web pages typically contain a large amount of information that is not part of the main content of the pages, e. g. banner ads, navigation bars, copyright notices, etc. Such noise on web pages usually leads to poor results in Web Mining which mainly depends upon the web page content. Therefore, it becomes very essential to extract information from the bulks of data and structure them into useful knowledge that will be helpful for some type of understanding. This leads to the birth of data mining. Web usage mining is the subject field of Data Mining which deals with the discovery and analysis of usage patterns from web data specifically web logs in order to improve the web based applications. The motive of mining is to find users' access models automatically and quickly from the vast Web log data, such as frequent access paths, frequent access page groups and user clustering. Through web usage mining, the server log, registration information and other relative information left by user provide foundation for decision making of organizations.

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