International Journal of Advance Engineering and Research Development (IJAERD) Volume 1,Issue 5,May 2014, e-ISSN: 2348 - 4470 , print-ISSN:2348-6406 @IJAERD-2014, All rights Reserved 1 A Web Page Recommendation system using GA based biclustering of web usage data Raval Pratiksha M. 1 , Mehul Barot 2 1 Computer Engineering, LDRP-ITR,Gandhinagar,cepratiksha.2011@gmail.com 2 Computer Engineering, LDRP-ITR,Gandhinagar, mpbarot@ldrp.ac.in Abstract-- The World Wide Web store, share, and distribute information in the large scale. There is large number of internet users on the web. They are facing many problems like information overload due to the significant and rapid growth in the amount of information and the number of users. As a result, how to provide web users with more exactly needed information is becoming a critical issue in web applications. 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 process of extracting useful knowledge from the server logs. This useful knowledge can be applied to target marketing and in the design of web portals. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. In this paper we are introducing a new approach for web page recommendation and user profile generation. This approach makes use of evolutionary biclustering technique for web page recommendation. We have applied it on two different datasets. One is clickstream data and other is web access log file of KSV University. The final results are generated from optimal biclusters obtained from evolutionary biclustering. Keywords- Web Mining, Usage Mining, Recommender system, Target Marketing, Biclustering. I. INTRODUCTION With the rapid growth of WWW, it becomes very important to find the useful information from this huge amount of data. The Web also contains the rich and dynamic collection of hyperlink information and Web page access and usage information, providing sources for data mining. The Web poses great challenges for effective knowledge discovery and data mining application. Web mining is defined as application of data mining techniques to automatically discover and extract information form Web documents and services. In general, Web mining is a common term for three knowledge discovery domains that are concerned with mining different parts of web: Web Structure Mining, Web Content Mining and Web Usage Mining. While Web Structure and Content mining utilize real or primary data on the Web, Web usage mining works on secondary data such as Web server access logs, proxy server logs, browser logs, user profiles, registration data, user sessions or transactions, cookies, user queries and bookmark data. The continuous growth of World Wide Web and available data in that domain imposes new design and development of efficient Web Usage Mining process. Web Usage Mining refers to the application of data mining technique to discover usage patterns in order to understand and better serve the needs of Web based applications. As Web data is unstructured it becomes more difficult to find relevant and useful information for Web users. Thus one of the goal of Web Usage Mining is to guide Web users to discover useful knowledge and to support them for decision making. In this paper we are focusing on recommendation system, one of the best applications of web usage mining. We are using a new approach of evolutionary biclustering for web page recommendation. Biclustering is a data mining technique which allows simultaneous clustering of the rows and columns