基于邻域多粒度粗糙集的知识发现模型

Knowledge Discovery Model Based on Neighborhood Multi-granularity Rough Sets

针对现有邻域多粒度粗糙集的定义及相应知识发现算法的不足,重新建立基于邻域多粒度粗糙集的知识发现模型.首先构建了多邻域半径下的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,讨论了相关性质;然后定义了邻域多粒度粗糙集的粒度重要性,并构造了粒度约简算法;最后通过实例解释了算法的运行机制,验证了算法的有效性.

It is the purpose of the present work to re-establish a knowledge discovery model based on neighborhood multi-granulation rough sets from the perspective of the deficiency with respect to the existing definition of neighborhood multi-granulation rough sets and the corresponding knowledge discovery algorithms.We firstly constructed the optimistic neighborhood multi-granulation rough set model and pessimistic neighborhood multi-granulation rough set model under multiple neighborhood radii,and discussed several pertinent properties.Then we gave a definition for the granularity importance of neighborhood multi-granulation rough sets,and constructed a granularity reduction algorithm.Finally we conducted a demonstration for the acting mechanism of the proposed algorithm by using an example,and veri-fied its validity.