一种分布环境中最大频繁项目集挖掘算法

挖掘最大频繁项目集是数据挖掘中的重要研究课题。目前已经提出的最大频繁项目集挖掘算法大多是基于单机环境的，在分布环境中挖掘最大频繁项目集的算法尚不多见。文章提出了一种基于分布数据库的并行挖掘最大频繁项目集的算法。该算法尽可能地让每个处理器独立地挖掘，采用频繁模式树（FP—tree）作为数据结构，可方便地从各局部FP-tree中挖掘局部最大频繁项目集及判断各项目集的支持度。采用传递候选最大频繁项目集的方法。实验表明该算法是有效的并行算法。

Algorithm of Mining Maximum Frequent Pattern in Distributed Environment

Mining maximum frequent itemsets is a key problem in data mining research.There are many algorithms of Mining maximum frequent itemsets based on local databases.Very little work has been done in distributed databases.Therefore,algorithm of mining maximum frequent itemsets in distributed databases is proposed.It attempts to make each processor to do independently.It adopts frequent pattern tree(FP-tree).It can mine locally maximum frequent itemsets easily from locally FP-tree and count support of each pattern easily.It transmits candidate pattern.Experimental results show that the algorithm is effective parallel algorithm.