Performance of Novel Based Fuzzy Association Rules to Reduced Computational Data Sets by Row Counts
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 4
Abstract
It is a big challenge to predict the result of large data sets quickly. To overcome this problem we have proposed a novel algorithm to reduce the large data sets to smaller one without affecting the result of fuzzy association rule by Row count algorithm. Row counts and column counts play a vital role to output the result of association rule. It behaves like a two way filter of the data. Further the comparison is done among Different fuzzy association rule ,A fuzzy AprioriTid mining algorithm with reduced computational time [9] and CPDA Based Fuzzy association rules for learning achievement mining, 2009 international conference on machine learning and computing IPCSIT [15] (mention specific names) and proposed algorithm. The performance of each is shown using graphs and data sets.
Authors and Affiliations
Rohit Miri, Priyanka Tripathi, Vinay Soni, Asha Miri
Keywords
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- EP ID EP22042
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