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

作　　者：程昳 刘勇[3] CHENG Yi;LIU Yong(College of Computer Science,Sichuan University,Chengdu 610000,China;Department of Information and Engineering,Sichuan College of Architectural Technology,Chengdu 610000,China;Department of Electrical Engineering,Sichuan College of Architectural Technology,Deyang,Sichuan 618000,China)

机构地区：[1]四川大学计算机学院,成都610000 [2]四川建筑职业技术学院信息工程系,成都610000 [3]四川建筑职业技术学院电气工程系,四川德阳618000

出　　处：《计算机科学》2019年第6期224-230,共7页Computer Science

基　　金：国家自然科学基金(61071162)资助

摘　　要：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.

关 键 词：多粒度 粗糙集 邻域 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]