在粗糙集理论及粗糙模糊集理论中，上下近似及边界的求解与决策表属性约简是它们的核心内容。
In the rough set theory and rough-fuzzy set theory, computation of approximations and edge and attributes reduction of decision table is import part of them.
其主要内容包括近似集、决策系统、数据预处理以及属性约简等等，是一种处理不完整，不精确数据的有效方法。
The main contents include the approximation set, decision systems, data preprocessing and attribute reduction and so on. It is a effective method of dealing with incomplete, inaccurate data.
目前，人们对广义粗糙集的研究主要集中在集合的近似计算上，而真正利用广义粗糙集进行属性约简的研究还很少见。
The existing researches on generalized rough sets are mainly concentrated on set approximations; less effort has been made for attribute reduction.
为了获得简明的规则集，通常希望能找出最小的属性约简集，而求解最小约简是NP难问题，解决此类难题通常采用启发式算法以求得近似最优解。
The minimum attributes reduction set is expected to acquire the brief regulated set. This is taken as NP-hard Problem, which can be figured out through the heuristic algorithm.
提出约简质量的定义，从属性约简率和近似质量两方面来衡量约简效果。
The attribute reduction quality, which includes reduction ratio and approximate quality, is defined to scale the reduction effectiveness.
从拓扑的包含关系这一全新的视角进一步认识协调近似表示空间的属性约简理论。
The inclusion relation of topologies is used to recognize the theory of reduction in consistent approximate representation space.
从拓扑的包含关系这一全新的视角进一步认识协调近似表示空间的属性约简理论。
The inclusion relation of topologies is used to recognize the theory of reduction in consistent approximate representation space.
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