Multi-objective genetic algorithm based method for mining optimized fuzzy association rules

被引：0
|
作者：
Kaya, M [1 ]
Alhajj, R
机构：
[1] Firat Univ, Dept Comp Engn, TR-23119 Elazig, Turkey
[2] Univ Calgary, ADSA Lab, Calgary, AB, Canada
[3] Univ Calgary, Dept Comp Sci, Calgary, AB, Canada
来源：
INTELLIGENT DAA ENGINEERING AND AUTOMATED LEARNING IDEAL 2004, PROCEEDINGS | 2004年 / 3177卷
关键词：
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：
This paper introduces optimized fuzzy association rules mining. We propose a multi-objective Genetic Algorithm (GA) based approach for mining fuzzy association rules containing instantiated and uninstantiated attributes. According to our method, fuzzy association rules can contain an arbitrary number of uninstantiated attributes. The method uses three objectives for the rule mining process: support, confidence and number of fuzzy sets. Experimental results conducted on a real data set demonstrate the effectiveness and applicability of the proposed approach.
引用
收藏
页码：758 / 764
页数：7
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