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由于可以从多粒度、多层次的角度对名词型和数值型属性并存的混合数据进行有效处理,邻域多粒度粗糙集模型受到了广泛关注.为了有效降低属性约简计算过程中的迭代次数,实现邻域多粒度粗糙集模型的快速属性约简算法,基于双重粒化准则,深入分析不同属性子集序列和邻域半径对正域的影响,结合正域在属性子集和邻域半径共同作用下的单调性,提出一种基于双重粒化准则的邻域多粒度粗集快速约简算法,并通过理论分析与实例对比验证了算法的有效性和优越性.
Due to the multi-granularity and multi-level perspective, the neighborhood multi-granularity rough set model has been widely concerned about the effective processing of mixed data with the coexistence of noun and numeric attributes.In order to reduce the number of iterations in attribute reduction, Based on the dual granularity criterion, this paper deeply analyzes the influence of different attribute subsets and neighborhood radius on the positive domain, and combines the positive domain with the property subset and the radius of the neighborhood This paper proposes a fast neighborhood reduction algorithm based on double granularity criterion for multi-granularity rough sets, and verifies the effectiveness and superiority of the algorithm through theoretical analysis and example comparison.