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There are billions of web pages available on the World Wide Web (WWW). So there are lots of search results corresponding to a user¿s query out of which only some are relevant. The relevancy of a web page is calculated by search engines using page ranking algorithms. Most of the page ranking algorithm use web structure mining and web content mining to calculate the relevancy of a web page. In this thesis, we provide an extension to standard Weighted PageRank algorithm by combining web structure mining with web usage mining. The proposed method takes into account the importance of both the number of visits of inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. So, the resultant pages are displayed on the basis of user browsing behavior.