Biclustering of web usage data using Genetic Algorithm

Abstract

Internet is a source of large amount of Data having large number of internet users on the web. Now days the
users are facing many problems like information overload due to large number of internet users and rapid growth in the
amount of information. The solution to this problem is to provide users with more exactly needed information. Mining is the
process of extracting useful data from large database. Web mining extracts interesting pattern or knowledge from web data.
It is classified into three types as web content mining, web structure, and web usage mining Web usage mining is the
nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or
methods. It may give information that is useful for improving the services offered by web portals and information access and
retrieval tools. In this study, we propose a novel biclustering algorithm based on genetic algorithms (GAs) to effectively
segment the web usage data. In general, GAs is believed to be effective on NP-complete global optimization problems, and
they can provide good near-optimal solutions in reasonable time. Thus, we believe that a biclustering technique with GA
can provide a way of finding the relevant clusters more effectively. In this work Genetic Optimization technique is
combined with biclustering approach to propose a recommendation system using GA based biclustering of Web Usage
Data.