数据库中基于粗糙集的分类和约简

把粗糙集理论与基于概率统计ID3算法结合建立粗糙集约简模型，可处理不精确扣模糊数据集信息，多维属性元组组成的粗糙集数据可用公式描述论域、有限属性、条件属性、决策属性、属性值集扣信息函数，用数据表格描述对象以建立知识表达系统，粗糙集分类约简用ID3算法可消除冗余数据集扣冲突检测，用粗糙集理论处理不一致扣不确定数据集可得到知识等价类，通过ID3算法对比决策树，可导出数据集确定性规则扣不确定性约简规则。

Classification and Reduction in Database Based on Rough Sets

The rough sets reduction model is established by integrating rough sets theory with ID3 algorithm based on statistics, uncertainty fuzzy data set information can be processed with the model. The rough sets data consisted with multi-valued attributes group can be described by the universe, finite set of attribute, condition attribute, decision attribute, domain of attributes and information function. To build up knowledge expression system which object and attribute can be respectively described with data form. The redundant data sets and conflicting data can be eliminated with rough sets classification and reduction based on ID3 algorithm. Uncertainty data sets is processed with rough sets theory to knowledge equivalent class. Decision tree compared with ID3 algorithm, certainty rules of data sets and uncertainty reduction rules can be induced.