代数约简的知识粒度表示及其高效算法

Knowledge granularity representation and efficient algorithm of algebraic reduction

首先提出了修正相对粒度计算公式,给出其单调性证明以及等号成立的充要条件;然后证明了保持修正相对粒度不变是保持正区域不变的充要条件,并给出代数约简的知识粒度表示;最后讨论了现有相对粒度与修正相对粒度之间的关系,利用修正相对粒度的单调性给出计算属性重要性定义及其递归计算公式,进而利用基排序思想计算等价类,设计出一种计算决策表代数约简的高效算法.实验结果表明该算法是可行且高效的.

Firstly, a modified relative knowledge granularity is proposed. Its monotonicity is proved, and the necessary and sufficient conditions for equality are given. It is demonstrated that remaining the modified relative knowledge granularity and positive region unchanged is a necessary and sufficient condition for each other. Then the main concepts of algebraic reduction are described by knowledge granularity. The relation between existing relative knowledge granularity and its improvement is discussed. By modified relative knowledge granularity, an attribute relative significance is defined, and its recursive computing formula is presented. Then a heuristic attribute reduction algorithm based on this significance is designed, whose equivalence is computed by radix sort. The experimental results show that the algorithm is feasible and efficient.