基于加权粒度和优势关系的程度多粒度粗糙集近似集的动态并行更新算法  

Dynamic Parallel Updating Algorithm for Approximate Sets of Graded Multi-granulation Rough Set Based on Weighting Granulations and Dominance Relation

作　　者：赵艺琳 姜麟[1] 米允龙 李金海[1,3] ZHAO Yi-lin;JIANG Lin;MI Yun-long;LI Jin-hai(Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China;School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 101408,China;Data Science Research Center,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学理学院,昆明650500 [2]中国科学院大学计算机与控制学院,北京101408 [3]昆明理工大学数据科学研究中心,昆明650500

出　　处：《计算机科学》2018年第10期11-20,共10页Computer Science

基　　金：国家自然科学基金(61562050;KKGD201707071);云南省教育厅基金(KKJB201707008)资助

摘　　要：随着大数据集的不断更新,经典的多粒度粗糙集理论不再适用。为此,提出加权粒度优势关系程度悲观多粒度粗糙集与加权粒度优势关系程度乐观多粒度粗糙集的相关理论。在此基础上,给出了一种基于加权粒度和优势关系的程度多粒度粗糙集近似集的动态并行更新算法。最后,通过实验验证了所提算法的有效性,其能够应对海量动态更新的数据变化并提升运行效率。With the continuous updating of large data sets,the classical multi-granulation rough set theory is no longer practical.Therefore,this paper put forward the related theory of graded pessimistic multi-granulation rough set with weighting granulations and dominance relation,graded optimistic multi-granulation rough set with weighting granulations and dominance relation.On the basis of this improved theory,this paper proposed a dynamic parallel updating algorithm for approximate sets of graded multi-granulation rough set based on weighting granulations and dominance relation.Finally,the experiment verifies the effectiveness of the proposed algorithm,which is able to handle data with massive dynamic updates and improve running efficiency.

关 键 词：多粒度粗糙集 加权 优势关系 并行更新算法 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]