一种自适应的模糊关联规则挖掘算法

An Adaptive Algorithm for Mining Fuzzy Association Rules

关联规则是数据挖掘的重要研究内容之一。传统的关联规则挖掘算法仅适于处理二元属性与分类属性。为更好地处理数量属性,提出了一种自适应的基于模糊概念的量化关联规则挖掘算法。该算法克服了传统的离散分区法的不足,改进了已有模糊关联规则支持度的计算方法。引入了一种基于聚类的隶属函数自动生成方法,使得模糊关联规则的发现不依赖于人类专家给出的隶属函数,使得关联规则的表示自然、简明,有利于专家理解。实验表明该算法是有效的。

Mining association rules is one of the important research problem in data mining, many algorithms have been proposed to find association rules in database with binary attribute and categorical attribute. Introduee an adaptive algorithm for mining fuzzy association rules. It overcomes the drawbacks caused by the traditional discrete interval method. The algorithm adopts an improved calculating measure of itemset. A method for automatie definition for membership funetion is proposed, whieh using fuzzy elustefing from training example. The experimental results show that the algorithm is effective and can provide important mining results to users.