基于线性表的闭频繁项集挖掘算法

Mining closed frequent itemsets based on list structure

利用频繁模式表的线性表简单结构及闭频繁项集挖掘的优点,提出了一种闭频繁项集挖掘算法.模式签名向量间的合取操作以及向量计数操作为该算法的主要操作,实现从已生成的闭频繁项集生成集中抽取代表模式,降低了模式搜索空间,简化了生成闭频繁项集的过程,实验结果验证了该算法的有效性.

An approach that combines the advantages of the frequent pattern list（FPL） and closed frequent itemsets mining was proposed.The approach selects representation patterns from closed frequent candidate itemsets to reduce combinational space of frequent patterns.By performing two operations,signature vector conjunction and vector counting,it simplifies the process of closed itemsets generation.Experimental results verified the effectiveness of the algorithm.