ABSTRACT

The World Wide Web has become one of the most important media to store, share and distribute information. The rapid expansion of the web has provided a great opportunity to study user and system behavior by exploring web access logs. Web Usage Mining is the application of data mining techniques to large web data repositories in order to extract usage patterns. Every web server keeps a log of all transactions between the server and the clients. The log data which are collected by web servers contains information about every click of user to the web documents of the site. The useful log information needs to be analyzed and interpreted in order to obtain knowledge about actual user preferences in accessing web pages. In recent years several methods have been proposed for mining web log data. This paper addresses the statistical method of Poisson distribution analysis to find out the higher probability session sequences which is then used to test the web application performance. The analysis of large volumes of click stream data demands the employment of data mining methods. Conducting data mining on logs of web servers involves the determination of frequently occurring access sequences. A statistical poisson distribution shows the frequency probability of specific events when the average probability of a single occurrence is known. The Poisson distribution is a discrete function wich is used in this paper to find out the probability frequency of particular page is visited by the user.