针对差别矩阵求约简过程中合取范式向析取范式等价转换的难题，提出一种基于差别矩阵构造约简树的有效方法。
Aiming at the problem of equivalent conversion from conjunctive normal form to disjunctive normal form, an effective algorithm was proposed to construct reduction tree based on discernibility matrix.
粗糙集理论中的值约简和数据挖掘领域中的决策树都是有效的分类方法，但二者都有其局限性。
Value reduction in rough set theory and decision tree in data mining are effectively used in the classification, but each of them has shortcomings.
本文从属性值缺失的填补、属性约简和决策树分支属性选择三方面进行研究。
The attribute missing values filling, attribute reduction and the choice of decision tree branch attributes are researched in this paper.
本文从属性值缺失的填补、属性约简和决策树分支属性选择三方面进行研究。
The attribute missing values filling, attribute reduction and the choice of decision tree branch attributes are researched in this paper.
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