一般二元关系下基于粗糙隶属函数的程度粗糙集

The Graded Rough Set based on Membership Function in General Binary-relation

在分析经典粗糙集模型不足的基础上,通过引入精度系数k(k∈(0.5,1]),给出了一般关系下基于粗糙隶属函数的程度粗糙集,并讨论了所给模型的相关重要性质。与经典粗糙集模型相比较,发现该模型不仅是对经典粗糙集模型的拓展,还是对基于等价关系的变精度粗糙集模型的拓展。

Upon scribing the limits of classical rough set model, the graded rough set model based on the rough membership function is established by introducing the precision coefficient and some important properties of it are discussed. Its key issue is that the graded rough set model can be seen not only as a expansion of classical rough set model but also as a variable precision rough set model based on general binary-relation. These results will be very helpful for the research of rough set model expansion and significant for establishing a framework of knowledge discovering in database system.