比较两种挖掘正态关联规则方法

Comparing Two Methods of Mining Normal Association Rules

首先采用两种聚类方法确定正态模糊数的两个参数 ,并借助正态模糊数模型来软化数量属性论域的划分边界 ,由此生成一系列的正态关联规则。接着给出正态关联规则的挖掘方法 ,此方法能挖掘出所有有意义的正态关联规则。最后对两种挖掘正态关联规则的方法进行了比较。

In this paper, two parameters of normal fuzzy numbers are determined by two clustering methods. Then the normal fuzzy number model is adopted to soften the domain partition boundary of the quantitative attributes and a series of linguistic value association rules are generated. The method of mining normal association rules is also provided. With this method, all interesting normal association rules can be mined. Two methods of mining normal association rules are compared in the last part.