基于邻域的可变粒度粗糙集模型

Variable Granulation Neighborhood Rough Set Model

分析传统粗糙集和邻域多粒度粗糙集的不足之后,在克服乐观多粒度粗糙集模型较为宽松,悲观多粒度粗糙集模型较为严格的缺点的基础上,为使粗糙集理论能从多粒度计算的角度,对名义型和数值型并存的混合型数据进行处理,将邻域多粒度粗糙集模型与可变粒度粗糙集模型相结合,提出了基于邻域的可变粒度粗糙集模型,定义了其下上近似,研究了基于邻域的可变粒度粗糙集模型的一些性质,并证明了基于邻域的可变粒度粗糙集模型是乐观邻域多粒度粗糙集模型和悲观邻域多粒度粗糙集模型的泛化,最后通过实例验证了模型的有效性.

By analyzing the weakness of the traditional rough set model and Neighborhood Multi-granulation rough set model,based on overcoming the weakness of optimistic multigranulation rough set model and pessimistic multigranulation rough set model,Combining Neighborhood-based multigranulation rough set model and Variable granulation rough set,Variable Granulation Neighborhood rough set model is proposed. This model can make the rough set theory be calculated from the multi granularity perspective,and deal with the heterogeneous data including categorical attributes and numerical attributes. The formal definition of the upper and lower approximation sets of Variable Granulation Neighborhood rough set model were given. Furthermore,the properties of this rough sets modol was discussed,and that the Variable granulation Neighborhood rough set is the generalization of optimistic Neighborhood-based multigranulation rough set model and pessimistic Neighborhood-based multigranulation rough set model was proved,The effectiveness of the proposed model is verified by an example.