基于柔性逻辑的区分矩阵属性约简算法

传统的基于区分矩阵的属性约简算法只能处理离散数据，而绝大部分数据既包含离散属性又包含连续属性.针对这一问题，本文使用一种可以对离散数据和连续数据进行统一处理的方法.该方法利用柔性逻辑等价关系替代原来的不可分辨关系，简化了传统算法中的离散化过程，提高了算法效率.实验表明，与传统的算法相比，改进后算法省略了离散化这一过程，可以对离散数据和连续数据统一进行处理.

Attribute Reduction Algorithm of Discriminant Matrix Based on Flexible Logic

The traditional attribute reduction algorithm based on discriminant matrix can only deal with discrete data, and most of the data contains both discrete and continuous attributes. In response to this problem, this study uses a method that allows discrete data and continuous data to be processed uniformly. This method replaces the original indistinguishable relation with the flexible logic equivalence relation, simplifying the discretization process in the traditional algorithm and improving the efficiency of the algorithm. Experiments show that compared with the traditional algorithm, the improved algorithm omits the process of discretization, and can deal with discrete data and continuous data uniformly.