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由于经典的粗集理论不能处理原始数据中的遗漏信息,需要对这些数据进行补齐才能用于知识获取。本文针对已有的基于粗集理论的不完备系统补齐算法ROUSTIDA的缺陷,提出了基于量化相似关系模型的数据补齐算法,减少了在完备化过程中产生的临时中间信息系统,使更多的缺损数据得到科学填补,同时尽量避免可能导致的决策规则矛盾问题。
Since classical rough set theory can not handle the missing information in the original data, these data need to be padded in order to be used for knowledge acquisition. In this paper, aiming at the defects of ROUSTIDA, which is based on rough set theory, a new data complementing algorithm based on quantitative similarity relation model is proposed, which can reduce the temporary intermediate information system generated in the process of completeness and make more Of the missing data to be scientifically filled, while avoiding possible conflict of decision rules.