论文部分内容阅读
单向Sp-粗集中,具有属性集α的知识[x](R-元素等价类[x])具有这样的特征:若α内部分属性被删除,则[x]内的元素个数增加;利用这一特征,考虑属性被删除的随机性,给出Sp-下阶梯知识,Sp-下阶梯知识的依信度生成,Sp-下阶梯知识属性依赖的原理,给出Sp-下阶梯知识的属性依赖挖掘定理,Sp-下阶梯知识的状态识别算法。
In unidirectional Sp-rough sets, knowledge [x] (R-element equivalence class [x]) with attribute set α has the characteristic that if the intra-α attribute is deleted, the number of elements in [x] By using this feature, we consider the randomness of attribute deletion, and give the Sp-lower ladder knowledge, Sp-lower ladder knowledge’s dependability generation, Sp-lower ladder knowledge attribute dependency principle, Sp- The Attribute Dependency Mining Theorem, Sp-Next Ladder Knowledge Status Recognition Algorithm.