基于分辨矩阵和约简树的增量式属性约简算法

为了对动态变化的决策表进行高效属性约简处理，在改进的分辨矩阵的基础上提出一种基于约简树的增量式属性约简算法IRART，该算法首先根据序贯属性约简算法对原决策表构造约简树，然后求出新增对象的分辨向量，并利用此向量对约简树进行修整，从而快速得到新决策表的所有约简，最后通过示例证明了这种算法的有效性。与传统增量式属性约简算法相比，该算法避免了复杂的逻辑演算，提高了属性约简的更新效率，理论分析表明该算法是有效可行的。

Incremental algorithms for attribute reduction based on discernibility matrix and reduction tree

For the efficient attribute reduction of dynamic decision table,the incremental algorithm for attribute reduction based on discernibility matrix and reduction tree is proposed.This method builds reduction tree according to sequential attribute reduction algorithm,calculates discernibility vector of new object,and revises reduction tree according to discernibility vector.Thereby attribute reduction cluster of new decision table can be abtained quickly,finally the validity of the algorithm is proved by exampl...