Rough sets theory is emerging as a powerful tool as inducing knowledge classification knowledge from database.
粗合集合理论现在已成为数据库知识分类的一种强有力的工具。
In this paper, the generation of maximally generalized rules in the course of classification knowledge discovery based on rough sets theory is discussed.
讨论了基于粗糙集理论的分类知识发现中最大泛化规则的生成。
Then, the mistake classification rate between two fuzzy sets is defined. Later, fuzzy variable precision rough set model is proposed based on the two definitions.
另一方面提出了在一定错分率存在的条件下的模糊集合间的相对错分率，之后结合这两个理论给出了基于模糊邻域算子的模糊变精度粗糙集模型。
Then, the mistake classification rate between two fuzzy sets is defined. Later, fuzzy variable precision rough set model is proposed based on the two definitions.
另一方面提出了在一定错分率存在的条件下的模糊集合间的相对错分率，之后结合这两个理论给出了基于模糊邻域算子的模糊变精度粗糙集模型。
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