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,武汉430010

出　　处：《计算机科学》2001年第8期114-115,113,共3页Computer Science

摘　　要：In this paper,the generation of maximally generalized rules in the course of classitication knowledge discovery based on rough sets theory is discussed. Firstly, an algorithm is introduced. Secondly,we propose that the information-based J-measure is used as another measure of attribute signifi cance 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.In this paper, the generation of maximally generalized rules in the course of classitication knowledge discovery based on rough sets theory is discussed. Firstly,an algorithm is introduced. Secondly, we propose that the information-based J-measure is 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：最大泛化规则生成 粗糙集理论 分类规则 知识发现 人工智能 数据库 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]