Extracting exciting data and information related to server logs is an interesting area and the web usage mining caters to the need of the website positioning and marketing strategies to the site owners. The web log server creates log files regarding information about the page, IP address of the user, name of the browser, and OS used and time/date stamp regarding browsing patterns and this raw data is mined to extract useful information using web usage mining. The primary objective of this paper

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... is to find the frequently visited pages in a website in tandem using the server log files by combining clustering and sequential item mining techniques. This sequential item generator algorithm generates candidates using Apriori like approach and clustering technique discovers the user navigational behavior using the generated candidates effectively by employing equality distance. The proposed hybrid approach is compared with existing algorithms and a detailed comparison is carried out to prove the efficiency of the proposed algorithm.