| 摘要: |
| 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. |
| 关键词: 数据挖掘 相关的模糊c-方法算法 正态模糊数 语言值 关联规则 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(69931040) |
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| Mining Linguistic Value Association Rules |
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LU Jian-jiang,SONG Zi-lin,QIAN Zu-ping
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| Abstract: |
| 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. |
| Key words: data mining relational fuzzy c-means algorithm normal fuzzy number linguistic value association rule |
