Association rule mining for web usage data to improve websites

Association rule mining along with frequent items has been comprehensively research in data mining. In this paper, we proposed a model for association rules to mine the generated frequent k-itemset. We take this process as extraction of rules which expressed most useful information. Therefore, transactional knowledge of using websites is considered to solve the purpose. In this paper we use interestingness measure that plays an important role in invalid rules thereby reducing the size of rule data sets. The performance analysis attempted with Apriori, most frequent rule mining algorithm and interestingness measure to compare the efficiency of websites. The proposed work reduces large number of immaterial rules and produces new set of rules with interesting measure. Our extensive experiments will use relevant rule mining to enhance websites and data accuracy.