最大泛化规则生成  

Generation of Maximally Generalized Rules

作　　者：徐如燕[1] 鲁汉榕[2] 郭齐胜 

机构地区：[1]装甲兵工程学院,北京100072 [2]空军雷达学院指挥自动化工程系,湖北武汉430019

出　　处：《空军雷达学院学报》2001年第2期24-27,共4页Journal of Air Force Radar Academy

摘　　要：讨论了基于粗糙集理论的分类知识发现中最大泛化规则的生成。首先给出一个最大泛化规则的生成算法，提出采用基于信息论观点的Ｊ－ｍｅａｓｕｒｅ作为属性有效度量，用来在求最大泛化规则的过程中，启发式地选择试图删除的条件属性。最后通过实例说明了最大泛化规则生成算法的执行过程。In this paper, the generation of maximally generalized rules in the course of classification knowledge discovery based on rough sets theory is discussed. Firstly, an algorithm is introduced. We propose that the information-based J-measure be used as another measure of attribute significance value. This measure is used for heuristically selecting the conditions to be removed in the process of extracting a set of maximally generalized rules. Finally, we present an example to illustrate the process of the algorithm.

关 键 词：生成算法 泛化 规则 删除 粗糙集理论 知识发现 属性 度量 信息论 启发式 

分 类 号：TN914.41[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]