Mining high coherent association rules with consideration of support measure

doi:10.1016/j.eswa.2013.06.002

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Title:	Mining high coherent association rules with consideration of support measure
Authors:	Chen, Chun-Hao;Lan, Guo-Cheng;Hong, Tzung-Pei;Lin, Yui-Kai
Contributors:	淡江大學資訊工程學系
Keywords:	Data mining;Association rules;Propositional logic;Coherent rules;Highly coherent rules
Date:	2013-11-15
Issue Date:	2015-01-28 11:07:34 (UTC+8)
Publisher:	United Kingdom: Elsevier Science & Technology
Abstract:	Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as “if milk is bought, then cookies are bought”. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.
Relation:	Expert Systems with Applications 40(16), pp.6531-6537
DOI:	10.1016/j.eswa.2013.06.002
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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