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    题名: Mining high coherent association rules with consideration of support measure
    作者: Chen, Chun-Hao;Lan, Guo-Cheng;Hong, Tzung-Pei;Lin, Yui-Kai
    贡献者: 淡江大學資訊工程學系
    关键词: Data mining;Association rules;Propositional logic;Coherent rules;Highly coherent rules
    日期: 2013-11-15
    上传时间: 2015-01-28 11:07:34 (UTC+8)
    出版者: United Kingdom: Elsevier Science & Technology
    摘要: 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.
    關聯: Expert Systems with Applications 40(16), pp.6531-6537
    DOI: 10.1016/j.eswa.2013.06.002
    显示于类别:[資訊工程學系暨研究所] 期刊論文

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