基于兼存率与单项事务的关联规则优化算法

Association rule optimization algorithm based on CCP and TOI

文中研究基于兼存率(多个项同时存在的概率)与单项事务(仅包含一个项的事务)筛选提出关联规则优化算法ARO,通过对数据集D中每个项与事务T进行处理来过滤无用或干扰的数据,从而得出更加准确、显著的关联规则。实验结果表明,在标准数据集中,对比传统算法META,ARO算法在关联规则分析的显著性与准确性方面均有性能提升。

In this paper,an Association Rule Optimization Algorithm( ARO) is proposed based on MCP( Probability of multiple items coexisting) and TOI( a transaction has only one item). By eliminating useless or disruptive data with a selection strategy of dealing with each item in the datasets D and transaction T, more accutate and significant association rules are reached. Compared with META algorithm,the ARO algrithm has effectively improved the significance and accuracy of association rules.