Hybrid Modelling Of Web Usage Mining For Web Page Recommendation Based On Click Stream Data

Computer Engineering

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. A Recommender system is one of the best web usage mining Application which reduces the difficulties faced by the users to meet their requirements. It recommends the pages of interest to the user. With the rapid development of biclustering, more researchers have applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this study, we propose a novel biclustering algorithm based on genetic algorithms (GAs) to effectively segment the web usage data. In this work Genetic Optimization technique is combined with biclustering approach to propose a recommendation system using GA based biclustering of Web Usage Data. The main objective of this Algorithm is to retrieve the global optimal bicluster from the web usage data.

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