A Divide-and-Conquer Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports

被引：0
|
作者：
Chen, Chun-Hao [3 ]
Hong, Tzung-Pei [1 ,2 ]
Tseng, Vincent S. [3 ]
机构：
[1] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung 811, Taiwan
[2] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan
[3] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2008年
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：
Since items may have their own characteristics, different minimum support values and membership functions may be specified for different items. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is designed for finding minimum support values, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item's minimum support value and membership functions. The final best minimum support values and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.
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页码：1233 / +
页数：2
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