充分考虑了可辨识矩阵的特性，提出一种基于类别特征矩阵的决策规则提取算法。
An extracting rules algorithm based on class feature matrix is presented, which has made use of the characteristic of discernibility matrix.
接着对分布约简的可辨识矩阵中等价类的区分关系进行改进，提出一种新的近似约简算法。
Secondly, it introduces an improved algorithm which revises the discernibility relation of similarity sets on the basis of the Discernibility Matrix of distribution reduction.
本文基于可辨识矩阵提出一种连续属性离散化的方法，并利用平均互信息量对离散化结果进行修正。
The paper puts forward a method of discretization of continuous properties based on discernibility matrix and revises the discrete result by average mutual information.
提出了三种改进的属性约简算法：改进的代数集合算法、重要度加权平均算法和改进的可辨识矩阵算法。
And three modified attribute reduction algorithms are presented, including modified algebraic algorithm, weighed sum of attribute significance algorithm and modified discernible matrix algorithm.
基于粗糙集理论的不完备数据分析方法，以可辨识矩阵作为算法的基础，提出了一种改进的不完备数据分析方法。
Based on an incomplete data analysis method of the rough set theory and the distinguish matrix, bring forward an improved ROUSTIDA algorithm.
基于粗糙集理论的不完备数据分析方法，以可辨识矩阵作为算法的基础，提出了一种改进的不完备数据分析方法。
Based on an incomplete data analysis method of the rough set theory and the distinguish matrix, bring forward an improved ROUSTIDA algorithm.
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