Efficiently reducing the size of web log data using Fuzzy Dynamic Approach

Abstract

WWW is a huge repository of information which is growing exponentially. More and more people visit various web sites and search engines to find relevant information. To provide the huge information is not the problem, but the problem is that day by day more and more people having different needs and requirements search through this huge WWW and get lost in complex web structures and hence miss their inquiry goals. Web personalization can be the solution to this problem. Web personalization is the process where web site contents are tailored as per the needs of a user. For the personalization, the interesting access patterns can be mined from web usage data. In many applications of web personalization, dynamic recommendations of items are made based on user's browsing behavior and his/her profile. The regular explosion of e-Commerce, there is strong competition amongst companies and other sectors to be a focus for the customers. Web server analysis is very difficult to find out the web user behavior for any organization. It is useful for future web site improvement and design. In this paper proposed a Fuzzy dynamic approach for finding the web user session clusters from web log data. Direct elimination of the small-sized estimated sessions may bring about loss of an essential measure of data specially when small session large in number. This proposes a "Fuzzy Dynamic" approach to deal with manage this issue.