多重粗糙模糊集模型

为了充分揭示知识颗粒间的重叠性、对象的重要度差别及其多态性，基于多重集合，对Dubois粗糙模糊集意义下的粗糙模糊集模型的论域进行了扩展，提出了基于多重集的粗糙模糊集模型，给出了该模型的完整定义、相关定理和重要性质，其中包括多重粗糙模糊近似集、近似精度和可定义集的定义及其各种性质的证明、多重集意义下的粗糙模糊近似算子之间的关系及其与Dubois意义下的粗糙模糊近似算子之间的关系等。多重粗糙模糊集可用于从具有一对多依赖性关系的且具有模糊特性的数据中挖掘知识。

Multiple rough fuzzy set model

To fully describe the overlap among knowledge particles, significance difference among objects and polymorphism of objects, based on multi-set, an expansion was made on the domain of rough fuzzy set model in the sense of Dubois rough fuzzy set, multiple rough fuzzy set model was put forward, their corresponding definitions, theorems and properties were fully described, which included the definitions of multiple rough fuzzy approximate sets, approximation accuracy and definable sets, proofs of their important properties, relations among rough approximation operators in multiple rough sets and relations between Dubois rough fuzzy sets and multiple rough fuzzy sets. Multiple rough fuzzy sets can conveniently find associated knowledge from data with one-to-many dependency and fuzzy properties.