确定服务属性的相对重要性是顾客满意度测量的最重要的目标之一。

Determining the relative importance of service attributes is one of the most important objectives of customer satisfaction measurement.

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粗糙集理论是一种新的数据挖掘算法，文章以属性依赖重要性作为启发信息提出了一种新的属性约简算法，且加入了一定的分类正确度。

In this paper, we propose a new attributes reduction algorithm based on the significance of attribute dependencies as heuristic information and add a certain variable precision.

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目前粗糙集决策表中条件属性的重要性基本上是用条件属性的依赖度进行评判的。

At present basically significance of conditional attribute in the decisional table based on rough sets is evaluated through dependability of conditional attribute.

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