Effective Clustering Approach to Discover Outliers in Voluminous Database using Clustering Approach

In recent advancements of the internet have changed the business scenario and plethora of data is available for the decision making and to improve business transactions. Mining interesting data related to server logs is an evolving area and web usage mining caters to the need of the website positioning and marketing strategies. The web server creates log files regarding details about the page, IP address of the user, browser, and operating system used and time/date stamp regarding browsing patterns and this data is mined to extract useful information using web usage mining. The primary objective of this research work is to discover the low hit pages of a website from the log files. The research work mainly focuses on a new technique to find the browsing patterns or the navigational behavior of the users after mining the content of the server log files using a hybrid data mining techniques. The proposed algorithm uses sequential frequent item set mining technique. The proposed methods are evaluated using voluminous benchmarked datasets and from the experimental results, it is founds that the proposed methods are superior with respect to the accuracy, time consumption, memory usage and precision than the existing state of the art algorithms.