Bayesian Network Structure Learning Based on Rough Inclusion

机译：基于粗糙包容的贝叶斯网络结构学习

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A kind of Bayesian network structure learning approach based on rough inclusion is put forward. First of all, the idea of the apriori algorithm is applied to mine frequent attribute sets through restraining support. Then, inclusion theory of rough set is

机译：提出了一种基于粗糙夹杂物的贝叶斯网络结构学习方法。首先，通过抑制支持将APRiori算法的思想应用于Mode频繁属性集。然后，包含粗糙集的理论是

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来源
《Workshop on Intelligent Information Technology Application》|2007年||共4页
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作者
Yu-ling Li; Qi-zong Wu;
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中图分类 TP18-53;
关键词
Bayes methods; data mining; learning (artificial intelligence); rough set theory; Bayesian network structure learning; apriori algorithm; associated rules; frequent attribute set mining; restraining support; rough inclusion; rough set;

机译：贝叶斯方法;数据挖掘;学习（人工智能）;粗糙集理论;贝叶斯网络结构学习;APRIORI算法;相关规则;频繁的属性设定挖掘;抑制支持;粗糙的夹杂物;

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Bayesian Network Structure Learning Based on Rough Inclusion

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