A High Coherent Association Rule Mining Algorithm

被引:39
作者
Chen, Chun-Hao [1 ]
Lan, Guo-Cheng [2 ]
Hong, Tzung-Pei [3 ,4 ]
Lin, Yui-Kai [3 ]
机构
[1] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
[2] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan
[3] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan
[4] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung 811, Taiwan
来源
2012 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2012年
关键词
data mining; association rules; propositional logic; highly coherent rules; SUPPORT;
D O I
10.1109/TAAI.2012.51
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as "If milk is bought, then bread is bought". However, such a rule may be misleading since customers that buy milk may not buy bread. In this paper, an algorithm for mining highly coherent rules that takes the properties of propositional logic into consideration is proposed. The derived association rules may thus be more thoughtful and reliable. Experiments are conducted on simulation data sets to demonstrate the performance of the proposed approach.
引用
收藏
页码:1 / 4
页数:4
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