讨论了大型数据库上数量属性的关联规则问题.为了软化论域的划分边界,应用相关的模糊c-方法(relationalfuzzyc-means,简称RFCM)算法确定正态模糊数的两个参数,并借助正态模糊数模型来划分数量属性的论域,由此生成一系列的语言值关联规则.另外，给出了语言值关联规则的挖掘方法.由于语言值能很好地表示抽象的概念,从而使得挖掘出的关联规则更抽象、更容易被人理解.

The issue of quantitative association rules in large databases is discussed in this paper. In order to soften partition boundary of the domain, the relational fuzzy c-means algorithm is adopted to determine two parameters of normal fuzzy numbers, then the normal fuzzy number model is adopted to partition the domain of the quantitative attributes and a series of linguistic value association rules are generated. The mining method of the linguistic value association rules is also provided. Because the abstract concepts can be well expressed with the linguistic values, the mined association rules are more abstract and easy to understand.

国家自然科学基金资助项目(69931040)