论文部分内容阅读
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.
Rough set theory is an important tool for data mining. In this paper, we study a class of generalized rough sets, which cover generalized rough set. The main results are as follows: (1) The basic properties of generalized Rough sets covering the classical Pawlak rough set theory ; (2) A sufficient and necessary condition for two covers to generate the same covering generalized rough set in the universe of discourse; (3) A covering reduction, that is, one covering can generate the smallest part of the original covering rough set; (4) The interdependence of rough set upper and lower approximation operations; (5) the axiomatization of the approximate operation under cover.