A Multi-objective Genetic-Fuzzy Mining Algorithm

被引：11
|
作者：
Chen, Chun-Hao [1 ]
Hong, Tzung-Pei [2 ,3 ]
Tseng, Vincent S. [1 ]
Chen, Lien-Chin [1 ]
机构：
[1] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan
[2] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung 811, Taiwan
[3] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2 | 2008年
关键词：
D O I：
10.1109/GRC.2008.4664771
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a multi-objective genetic-fuzzy mining algorithm for extracting both membership functions and association rules from quantitative transactions. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions. It consists of two factors, coverage factor and overlap factor, to avoid two bad types of membership functions. The second one is the total number of large I-itemsets from a, given set of minimum support values. The two criteria have a trade-off relationship. Experimental results also show the effectiveness of the proposed approach in finding the Pareto-front membership functions.
引用
收藏
页码：115 / +
页数：2
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