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Weighted items, Rough Set Theory, Apriori Algorithm, min. w-support, min. w-confidence, weighted association rule mining, the Genetic Algorithm
The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and observed that the approach works as per the expectation and will be beneficial in situation when there is a requirement for the consideration of hidden rules(maximizing generated rules) in decision-making process.
Shrikant Brajesh Sagar, Akhilesh Tiwari, "Rough Set and Genetic Based Approach for Maximization of Weighted Association Rules", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.3, pp.54-63, 2016. DOI:10.5815/ijmecs.2016.03.07
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