摘要： 针对粗糙集对于连续域属性决策表的处理能力差以及不容易获得模糊集之间关系等问题，提出一种基于连续型属性模糊关联规则约简算法。该算法引入三角隶属度函数将连续属性值转化为模糊值，并使用硬C均值聚类方法获得数据集之间关系，采用遗传算法优化该模型。仿真结果验证了该模型的有效性。

关键词: 模糊关联规则, 连续域, 遗传算法, 硬C均值

Abstract: Aiming at the problems of low processing capability of rough sets to continuous domain attribute decision table and not easy to obtain relationship of fuzzy sets, a new method of continuous attribute reduction algorithms is proposed, based on combining fuzzy set with rough set. Continuous attribute values are transformed into fuzzy values with triangular membership function. And Hard C-Means(HCM) clustering is used to obtain relationship among the fuzzy sets and Genetic Algorithms(GA) is used to optimize the model. Simulation results show the effectiveness of the proposed model.

Key words: fuzzy association rule, continuous domain, Genetic Algorithm(GA), Hard C-Means(HCM)

中图分类号: 

• TP301