Paper Details

Internet is a source of large amount of Data having large number of internet users on the web. Now days theusers are facing many problems like information overload due to large number of internet users and rapid growth in theamount of information. The solution to this problem is to provide users with more exactly needed information. Mining is theprocess 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 thenontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques ormethods. It may give information that is useful for improving the services offered by web portals and information access andretrieval tools. In this study, we propose a novel biclustering algorithm based on genetic algorithms (GAs) to effectivelysegment the web usage data. In general, GAs is believed to be effective on NP-complete global optimization problems, andthey can provide good near-optimal solutions in reasonable time. Thus, we believe that a biclustering technique with GAcan provide a way of finding the relevant clusters more effectively. In this work Genetic Optimization technique iscombined with biclustering approach to propose a recommendation system using GA based biclustering of Web UsageData.

Pratiksha Raval, Mayuree Rathva, Nishant Khatri, Nimit Modi, Pragna Makwana, “Biclustering of web usage data using Genetic Algorithm”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 13, pp. -, January 2017.