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Feature Selection by Mining Optimized Association Rules based on Apriori Algorithm

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 20
Year of Publication: 2015
Authors:
K. Rajeswari
10.5120/21186-3531

K Rajeswari. Article: Feature Selection by Mining Optimized Association Rules based on Apriori Algorithm. International Journal of Computer Applications 119(20):30-34, June 2015. Full text available. BibTeX

@article{key:article,
	author = {K. Rajeswari},
	title = {Article: Feature Selection by Mining Optimized Association Rules based on Apriori Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {20},
	pages = {30-34},
	month = {June},
	note = {Full text available}
}

Abstract

This paper presents a novel feature selection based on association rule mining using reduced dataset. The key idea of the proposed work 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. We 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. Experimental results of proposed algorithm on datasets from standard university of California, Irvine (UCI) demonstrate that our algorithm is able to classify accurately with minimal attribute set when compared with other feature selection algorithms.

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