教師資料查詢 | 類別: 期刊論文 | 教師: 陳俊豪 CHUN-HAO CHEN (瀏覽個人網頁)

標題:Mining high coherent association rules with consideration of support measure
學年102
學期1
出版(發表)日期2013/11/15
作品名稱Mining high coherent association rules with consideration of support measure
作品名稱(其他語言)
著者Chen, Chun-Hao; Lan, Guo-Cheng; Hong, Tzung-Pei; Lin, Yui-Kai
單位淡江大學資訊工程學系
出版者United Kingdom: Elsevier Science &; Technology
著錄名稱、卷期、頁數Expert Systems with Applications 40(16), pp.6531-6537
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.
關鍵字Data mining;Association rules;Propositional logic;Coherent rules;Highly coherent rules
語言英文
ISSN1873-6793
期刊性質國外
收錄於SCI;
產學合作
通訊作者Hong, Tzung-Pei
審稿制度
國別英國
公開徵稿
出版型式,電子版,紙本
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