基于粗糙集的发现决策偏好信息的新方法

A New RS-Based Method of Discovering Decision-preferential Information

传统的粗糙集(RoughSet)模型基于可利用信息的完全性,忽视了可利用信息中的不完全性和可能存在的统计信息,因而不能发现隐藏在大量决策实例中的不确定性决策偏好信息.为了解决这个问题,该文提出了一种将加权关联规则运用到粗糙集模型中的新方法.

Traditional rough set model is based on completion of available information,ignoring incompletion and statistical information of available information,so we can not discover uncertain decision-preferential information hiding in large numbers of decision-making instances by traditional rough set model.In order to resolve this problem,we develop a new method applying weight association rule into rough set model.