Fuzzy genetic algorithms for pairs mining

被引:0
|
作者
Cao, Longbing [1 ]
Luo, Dan [1 ]
Zhang, Chengqi [1 ]
机构
[1] Univ Technol Sydney, Fac Informat Technol, Sydney, NSW 2007, Australia
来源
PRICAI 2006: TRENDS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4099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
Pairs mining targets to mine pairs relationship between entities such as between stocks and markets in financial data mining. It has emerged as a kind of promising data mining applications. Due to practical complexities in the real-world pairs mining such as mining high dimensional data and considering user preference, it is challenging to mine pairs of interest to traders in business situations. This paper presents fuzzy genetic algorithms to deal with these issues. We introduce a fuzzy genetic algorithm framework to mine pairs relationship, and propose strategies for the fuzzy aggregation and ranking of identified pairs to generate final optimum pairs for decision making. The proposed approaches are illustrated through mining stock pairs and stock-trading rule pairs in stock market. The performance shows that the proposed approach is promising for mining pairs helpful for real trading decision making.
引用
收藏
页码:711 / 720
页数:10
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