摘要： 大部分关联规则挖掘算法使用同一最小支持度阈值进行挖掘，但在实际使用中由于各项目发生频率的不同，理应有不同的最小支持度支持。该文提出了一种多最小支持度关联规则挖掘算法，为每一项目设置一最小支持度，同时在生成备选集和最大频繁集的过程中使用最大值控制来实现剪枝，有效地提高了该算法的效率，最后用一个超市销售物品的例子来说明该算法的使用。

关键词: 关联规则；最大值控制；多最小支持度

Abstract: Most of the previous approaches of mining association rules set a single minimum support threshold for all the items or itemsets. But in real applications, different items have different occurrence frequencies. So the different items require vary minimum supports. This paper provides an approach of mining association rules with multiple minimum supports, that means each item has different minimum supports. The maximum constraint is used in a simple algorithm based on the apriori approach to find the large-itemsets and association rules. The proposed algorithm is easy and efficient. At the end, it has a mining example of supermarket database to explain this algorithm.

Key words: Association rule; Maximum constraint; Multiple minimum supports