杂合数据的粗糙集属性约简方法  

Rough Set Based Attribute Reduction Algorithm for Hybrid Data

作　　者：谭旭[1] 唐云岚[1] 张少丁[1] 陈英武[1] 

机构地区：[1]国防科技大学信息系统与管理学院,湖南长沙410073

出　　处：《国防科技大学学报》2008年第6期83-88,共6页Journal of National University of Defense Technology

摘　　要：针对决策表中属性取值为杂合数据的情况,提出了基于粗糙集理论的属性约简算法。首先给出了对象间在杂合数据下的相似度计算定义。为了获取合理的对象集合的软划分,给出了阈值计算的最优化模型,并基于粗糙集的上、下近似的概念,得到对象集合在条件属性下的上、下近似的覆盖划分。之后,通过各对象基于条件属性和决策属性的上、下近似下的分布矩阵描述,利用最大分布矩阵,直观地得到两种不同观点下的约简结果。实验结果表明了本算法的合理和有效性。With regard to the attribute values in decision table, which are described with hybrid data, a new algorithm of attribute reduction based on rough set theory is proposed. First, the similarity relations among objects with hybrid data are defined. In order to obtain reasonable soft partitions among objects, the optimization model for threshold accounting is presented. Then, based on the upper and lower approximation concept from rough set theory, the covering upper and lower similar partitions among objects are obtained. In succession, through descriptions of the upper and lower similar distribution matrixes found on condition attributes and decision attribute, the two attribute reduction results of different viewpoints can be retrieved intuitively, based on the max-distribution matrixes. Finally, the experiment results prove that this algorithm is effective and feasible.

关 键 词：杂合数据 属性约简 上 下近似覆盖划分 分布矩阵 粗糙集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]