Optimized Mining of Potential Positive and Negative Association Rules

被引:2
|
作者
Bemarisika, Parfait [1 ,2 ]
Totohasina, Andre [1 ]
机构
[1] Univ Antsiranana, Lab Math Informat, ENSET, Antsiranana, Madagascar
[2] Univ La Reunion, Lab Informat & Math EA2525, St Denis, France
来源
BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY, DAWAK 2017 | 2017年 / 10440卷
关键词
Association rules; Optimized extraction; Support-M-GK;
D O I
10.1007/978-3-319-64283-3_31
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The negative association rules are less explored compared to the positive rules. The existing models are limited to the structure of binary data requiring of the repetitive accesses to the context, and the traditional couple support-confiance which is not effective in the presence of the dense data. For that, we propose a new model of optimization by using a new structure of data, noted MatriceSupport, and a new more selective couple, support-M-GK.
引用
收藏
页码:424 / 432
页数:9
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