期刊名称：Bonfring International Journal of Software Engineering and Soft Computing
印刷版ISSN：2250-1045
电子版ISSN：2277-5099
出版年度：2017
卷号：7
期号：4
页码：01-04
语种：English
出版社：Bonfring
摘要：In data mining, an association rule is a pattern that states the occurrence of two items (premises and consequences) together with certain probability. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. This paper focuses on class association rules mining based on the approach of Rough Set Theory (RST). In addition, this paper presents an algorithm for finest class rule set mining inspired from Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation inspired from RST. The proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method
关键词：Data Mining; Rough Set Theory; Class Association Rule; Association Rule mining; NAR; Bitmap; Class Association Rules
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