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A Negative Association Rules for Web Usage Mining Using Negative Selection Algorithm

The immense capacity of web usage data which survives on web servers contains potentially precious information about the performance of website visitors. Pattern Mining involves applying data mining methods to large web data repositories to extract usage patterns. Due to the emerging reputation of the World Wide Web, many websites classically experience thousands of visitors every day. Examination of who browsed what can give necessary approach into the buying pattern of obtainable customers. Right and timely decisions made based on this acquaintance have helped organizations in accomplishing new heights in the market. The aim of discovering rules in Web log data is to obtain information about the navigational behavior of the users. This can be used for marketing purposes, dynamic creation of user profiles, finding user pattern navigation, etc. This suggested study, particularly focuses on examining an efficient algorithm for mining negative association rules from the clustered weblog navigational patterns.The algorithm indicates traditional association rules to include negative association rules to be an alternative for using positive association rule itself. When mining negative association rules take place, the same minimum support vestibule is used to mine frequent negative item sets. The rules gathered in such a manner are bridged by applying a negative selection algorithm, which helps in discovering all convincing negative association rules quickly and solves some of the limitations in traditional association rule mining.The experimental result reveal the proposed work to be highly effective.