QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules

被引：0
|
作者：
Salleb-Aouissi, Ansaf [1 ]
Vrain, Christel [2 ]
Nortet, Cyril [2 ]
机构：
[1] Columbia Univ, CCLS, 475 Riverside Dr, New York, NY 10115 USA
[2] Univ Orleans, LIFO, F-45067 Orleans 02, France
来源：
20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2007年
关键词：
Association rules; quantitative (numeric) attributes; unsupervised discretization; genetic algorithm;
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：
In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive data mining tool.
引用
收藏
页码：1035 / 1040
页数：6
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