Mining positive and negative fuzzy association rules

被引：0
|
作者：
Yan, P [1 ]
Chen, GQ
Cornelis, C
De Cock, M
Kerre, E
机构：
[1] Tsing Hua Univ, Sch Econ & Management, Beijing, Peoples R China
[2] State Univ Ghent, Fuzziness & Uncertainty Modelling Res Unit, B-9000 Ghent, Belgium
来源：
KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS | 2004年 / 3213卷
关键词：
fuzzy association rules; positive and negative associations; quantitative attributes;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
While traditional algorithms concern positive associations between binary or quantitative attributes of databases, this paper focuses on mining both positive and negative fuzzy association rules. We show how, by a deliberate choice of fuzzy logic connectives, significantly increased expressivity is available at little extra cost. In particular, rule quality measures for negative rules can be computed without additional scans of the database.
引用
收藏
页码：270 / 276
页数：7
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