An evolutionary method for associative local distribution rule mining
An evolutionary method for associative local distribution rule mining
复制标题
一种关联局部分布规则挖掘的进化方法
DOI：
10.1007/978-3-642-39736-3_19
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Takashi Hanioka
中科院分区：
文献类型：
--
作者：
Kaoru Shimada;Takashi Hanioka
提出了一种基于图结构的进化计算技术的连续值预测规则挖掘方法。该方法提取的规则称为关联局部分布规则，其结果部分具有连续值的窄分布。一组关联的局部分布规则应用于连续值预测。实验结果表明，该方法可以为连续值预测提供有用的规则。此外，基于关联局部分布规则定义了两种对比度规则。对比规则提取的性能进行了评估，结果表明，该方法有潜力实现两个数据集之间的对比分析。
A method for rule mining for continuous value prediction has been proposed using a graph structure based evolutionary computation technique. The method extracts the rules named associative local distribution rule whose consequent part has a narrow distribution of continuous value. A set of associative local distribution rules is applied to the continuous value prediction. The experimental results showed that the method can bring us useful rules for the continuous value prediction. In addition, two cases of contrast rules are defined based on the associative local distribution rules. The performances of the contrast rule extraction were evaluated and the results showed that the proposed method has a potential to realize contrast analysis between two datasets.