Abstract: This paper proposes a novel method to find quantitative association rules by clustering the transactions of a database into clusters and projecting them into the domains of the quantitative attributes to form meaningful intervals which may be overlapped. Experimental results show the method can efficiently find quantitative association rules, and can find important association rules which may be missed by the previous algorithms.

Key words: Data mining, Quantitative association rule, Frequent set, Cluster

摘要： 提出了一种新的挖掘量化关联规则的方法。该方法使用聚类算法把数据库中的交易记录分成若干个簇，把簇投影到数值型属性所在的域，形成重叠的、有意义的区间。实验结果显示，这种方法能够有效地挖掘量化关联规则，并且能够发现以前的算法可能遗漏的重要的规则。

关键词: 数据挖掘, 量化关联规则, 频集, 聚类

CLC Number: 

• TP311.13