多最小支持度策略的关联规则挖掘方法

关联规则的数据挖掘是当今数据挖掘领域的重要内容之一 .国内现有的关联规则挖掘算法大多是在最小支持度的限定条件下 ,发现挖掘数据的各属性间的所有关联型知识 .而事实上由于基于不同数据属性的事件的发生频率是不同的 ,这样仅通过唯一的支持度限定的挖掘就无法正确反映挖掘对象本身的特征 .本文从客观事实的本质出发 ,在原有 Apriori算法的基础上 ,采用了一种多支持度的关联规则挖掘策略 .最后通过实验结果表明 ,这种挖掘方法能够更加准确和高效地进行知识挖掘 .

An Approach of Mining Association Rules with Multiple Minimum Supports Stratagem

Mining association rules is an important topic in the data mining research. At present, the inland algorithms of mining association rules are almost achieved by the single minimum support. In fact, the occurrence frequencies of the events corresponding to each attribute are very different, so the restriction of single minimum support is able to reflect the mining objective. In this paper, by virtue of the inherent characters an approach of mining association rules with multiple minimum supports stratagem is improved on basis of Apriori algorithm. At the end, its efficiency is proved by the application on the diagnosis the wheat disease.