Feature Selection by Mining Optimized Association Rules Based on Apriori Algorithm
In this paper, the authors present a novel feature selection based on association rule mining using reduced dataset. This paper is to find closely related features using association rule mining method. Apriori algorithm is used to find closely related attributes using support and confidence measures. From closely related attributes a number of association rules are mined. Among these rules, only few related with the desirable class label are needed for classification. They have implemented a novel technique to reduce the number of rules generated using reduced data set thereby improving the performance of Association Rule Mining (ARM) algorithm.
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