中文摘要：

为了实现防空信息战效能的模糊评价，清除指标隶属度中对目标分类不起作用的冗余值，用基于熵的数据挖掘方法，通过挖掘隐藏在各指标隶属度中关于目标分类的知识信息，理清目标分类与指标隶属度之间的关系，定义区分权清除指标隶属度中对目标分类不起作用的冗余值，并提取有效值计算目标隶属度。防空信息战效能模糊评价的事例分析表明：具有多指标属性的决策问题，为了排除冗余值的干扰，则隶属度转换模型可作为有效的方法。基于熵的数据挖掘方法有效解决了模糊评价中冗余数据的干扰，从而解决了一大批多指标的决策问题。

英文摘要：

In order to realize fuzzy comprehensive evaluation of the information warfare of air defense and clear off the redundant data, in indexes membership, which are not useful for goal classification, and based on data mining of entropy, mining knowledge information about object classification hidden in every index, the relation between object classification and index membership is affirmed, the redundant data in index membership for object classification are eliminated by defining distinguishable weight, and valid values are extracted for computing object membership. A case of the information warfare of air defense is adopted in this paper. Empirical results show that ： In order to clear the interference of redundant data, the membership conversion model can be used as a valid method with multiple indexes attributes. By using the method based on data mining of entropy clear the interference of redundant data in fuzzy evaluation is cleared off and many multiple indexes decision - making problems are solved.