中文摘要：

粗糙集用上、下近似集刻画不确定目标集合，而粗糙集的近似集用0．5-近似集作为不确定目标集合的近似集．本文首先分析了基于粗糙集的0．5-近似集相似度的属性约简算法存在理论不完备的不足，指出这种相似度具有随知识粒度变化不敏感的缺陷．然后进一步给出了多粒度知识空间下相似度的变化规律，提出了粗糙集近似集的模糊度概念，分析了粗糙集近似集的模糊度在多粒度知识空间下的变化规律，进而提出了相应的属性约简算法．从新的视角构建了目标概念与其近似集的差异性度量方法．

英文摘要：

Rough set describes an uncertain target set with upper and lower approximation sets,and approximation set of rough set uses 0. 5-approximation set as an approximation set of the uncertain target set.In this paper,we firstly find that the theory of attribute reduction algorithm based on similarity between target set and its 0. 5-approximation set is still incom-plete,and this similarity is not sensitive to changing granularities.In order to overcome above shortcomings,the change rule of similarity with changing granularities in a multi-granularity space is analyzed,fuzzy degree of approximation set is de-fined,and the change rules of this fuzziness with changing granularities are analyzed in detail in a hierarchical space.Finally, a new attribute reduction algorithm is proposed.From a new perspective,a kind of differentiation measure between an uncer-tain target set and its approximation set is presented.