中文摘要：

针对Apriori算法必须耗费大量的时间来处理规模巨大的候选项目集等问题,提出一种基于数据依赖的关联规则挖掘算法ARMAFD。该算法利用函数依赖所隐含的属性间联系来缩小候选项目集的规模,提高了算法的效率,丰富了函数依赖的教学内涵,同时也为关联规则的挖掘提供了一种新的途径。实验结果表明ARMAFD算法是有效可行的。

英文摘要：

To solve the problems that the Apriori algorithm must spend a lot of time to deal with the large of candidate item sets,this paper put forward a mining algorithm of association rules based on functional dependency. The algorithm made use of the implicit relationship between attributes,which could shrink the scale of candidate item sets and improve the efficiency of the algorithm. The method might enrich the teaching connotation of functional dependency,and provide a new way of mining association rules. The experiments show that the algorithm is efficient.