一种分布式全局频繁项集挖掘方法

Mining algorithm of global frequent items in distributed database

提出一种基于频繁模式树与最大频繁项集的分布式全局频繁项集挖掘算法BFM-MGFIS,该算法引入子集枚举树以实现有序挖掘与全局剪枝策略,有效地减小了候选数据集且提高了并行性,实验表明本文提出的算法是有效可行的。

A kind of algorithm BFM-MGFIS（Based on Frequent-pattern tree and Most frequent items Mining Global Frequent Items Set） in distributed database is proposed.This algorithm introduces subset enumeration tree to relize mining orderly and pruning globally, not only greatly reducing candidate sets, but also promoting parallelism capacity.Experimental results show that the algorithm is effective.