摘要
应用经典的基于不可分辨关系的粗集理论对实际生产过程的监测数据进行处理、分析 ,并以此对生产过程质量进行评价时 ,无法解决待处理数据中某些属性定义域存在的优先关系和系统数据某种程度的缺失。针对该问题 ,提出了基于相似—优先关系的粗集扩展模型 ,通过相似—优先关系综合处理数据的不完整性和属性数据间的优先关系 ,改进的粗集模型不仅允许从原始的不完整数据表中直接进行数据处理和规则挖掘 ,而且规则的获取具有更大的灵活性 ,同时规则更易理解、更具归纳性。
It is impossible for the original rough set to deal with the data and their domains which came out of the production process and were preference-orders of attributes. In order to solve the problems, the paper presented a new model, which was based on similarity and preference-orders. This model could do data process and rule mining in the original incomplete source. It was more understandable and flexible for users because of its natural syntax.
出处
《机械设计与制造工程》
2002年第5期91-93,共3页
Machine Design and Manufacturing Engineering
