NDLI: Multiple-Step Rule Discovery for Associative Classification

Content Provider	IEEE Xplore Digital Library
Author	Tien Dung Do Siu Cheung Hui Fong, A.C.M.
Copyright Year	2009
Abstract	Associative classification has shown great promise over many other classification techniques. However, one of the major problems of using association rule mining for associative classification is the very large search space of possible rules which usually leads to a very complex rule discovery process. This paper proposes a multiple-step rule discovery approach for associative classification called Mstep-AC. The proposed Mstep-AC approach focuses on discovering effective rules for data samples that might cause misclassification in order to enhance classification accuracy. Although the rule discovery process in Mstep-AC is performed multiple times to mine effective rules, its complexity is comparable with conventional associative classification approach. In this paper, we present the proposed Mstep-AC approach and its performance evaluation.
Starting Page	365
Ending Page	369
File Size	292954
Page Count	5
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.150
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Degradation Machine learning algorithms Association rule mining Space technology Machine learning Associativeclassification Association rules Data mining Artificial intelligence Computational intelligence
Content Type	Text
Resource Type	Article

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