基于命题逻辑的关联规则挖掘算法L-Eclat

Propositional Logic-based Association-rule Mining Algorithm L-Eclat

关联规则挖掘是数据挖掘领域非常重要的课题,在很多领域被广泛应用。关联规则挖掘算法都需要设置最小支持度和最小置信度。很多国内外学者研究的挖掘算法在这两方面都存在着一些问题,不仅需要大量的领域知识来设置合适的最小支持度,而且其结果集庞大、用户不容易理解。针对关联规则挖掘算法存在的问题,将命题逻辑融合到关联规则算法Eclat中,设计出了基于命题逻辑思想的挖掘算法L-Eclat。实验结果表明,L-Eclat算法压缩了挖掘的规则集,减小了算法的时间消耗,且即使是非常小的支持度也可以得到高质量的关联规则,这在一定程度上解决了支持度设置的问题。

Association rule mining is an important topic in the field of data mining,and it has been widely used in lots of practical applications.Generally,association rule mining algorithms have to set the minimal support threshold and the minimal confidence threshold.But it is hard for most mining algorithms to set these two values.Not only is tremendous related knowledge needed to select the support threshold,but also the mining results are too large and difficult to understand.To solve these problems,the idea of propositional logic was introduced into Eclat,which is one of the classical association rule mining algorithms.We proposed logic-based association rule mining algorithm called L-Eclat.Then,we compared L-Eclat with Eclat.The results show that L-Eclat can optimize and compress the result rule sets at certain degree,and it results in less time consumption and high-quality association rules.Furthermore,L-Eclat can run with a smaller support threshold,and it decreases the dependence on the support threshold and avoids spending much time on choosing a suitable support threshold.