Efficient mining of Frequent Itemsets and Association Rules

Thomas, Varun Cyriac; Asha, P.
May 2014
Australian Journal of Basic & Applied Sciences;May2014, Vol. 8 Issue 7, p48
Academic Journal
Background: Data Association mining is not only for predicting but also for the reverent correlation between data. In medical filed correlated big data overruled very high and not get fast and permanent solution for finding its relation. No one is interested in using old method for finding relation or forecasting relation between data. Apriori algorithm is a popular method for association rule mining but it have lot of disadvantage. Objective: Based on this problem, this project developed new an efficient Hash based algorithm for finds the generated rules. The existing mining algorithms cannot perform efficiently due to high and repeated association rules. From the experimental results and discussions we assure that the proposed work can extend a wide support for Decision Support Systems and outperforms other existing algorithm. Conclusion: From all algorithm and its efficiency calculation we can understand main drawbacks of data association mining. And basic problem in data association. So this paper suggest a new hash based algorithm as solution for this problem. Also Apriori algorithm is one of the best data mining algorithm and its minor disadvantages can overcome through the various structural and timing process like PVARM algorithm. Hash based algorithm and its efficiency is not compare with other on the basis of time and accuracy. Also rules are more efficient and only best rules are mined. so we can concluded this as the best algorithm for future data mining process. s.


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