摘要
粗集理论是数据挖掘的一个重要工具,本文研究一类广义粗集,即覆盖广义粗集.主要的结果有:(1)与经典的Pawlak粗集理论相对应的覆盖广义粗集的基本性质;(2)一个论域上两个覆盖生成相同覆盖广义粗集的充分必要条件;(3)一个覆盖的约简,即一个覆盖能生成原覆盖广义粗集的最小部分;(4)覆盖广义粗集中上下近似运算的相互依赖性;(5)覆盖下近似运算的公理化.
A class of generalized rough sets called covering generalized rough sets is investigated in this paper. The main results obtained are: (1) certain characteristics of covering generalized rough sets corresponding to that of the Pawlak's rough sets; (2)the necessary and sufficient conditions for generating the same covering lower and upper approximation operations by two coverings of the same domain; (3)the minimum reduction of a covering with the same covering lower and upper approximation operations; (4)the dependency of the covering lower approximation operation and the covering upper approximation generated by one covering; and (5) the axiomiza-tion of the covering lower approximation.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2002年第1期6-13,共8页
Pattern Recognition and Artificial Intelligence
基金
海外杰出人才引入计划
国家杰出青年研究基金
关键词
约简
广义粗集
覆盖广义粗集
数据挖掘
粗集理论
人工智能
Rough Set, Covering, Covering Lower Approximation, Covering Upper Approximation, Reduc- tion
