分布式全局频繁项目集的快速挖掘方法

针对传统的分布式全局频繁项目集挖掘算法存在大量的候选项目集，且求全局频繁项目集的网络通信代价过高等问题，提出了一种分布式数据库的全局频繁项目集快速挖掘算法（FDMA）．该算法改进了频繁模式树（FP-树）的结构，将双向FP-树改为单向，每个节点只保留指向父结点的指针，减少了指针数，由此可节省1／3的树空间；同时通过传送用3个很小的数组表示的被约束子树，在此挖掘全局频繁项目集的过程中不再生成大量候选项目集或条件FP-树，从而减小了网络通信量，提高了挖掘效率．实验表明，所提算法的挖掘速度比传统的分布式数据库数据挖掘算法至少提高了1倍之多，随着数据库规模的增大，它的扩展性将更好．

Fast Mining Algorithm for Distributed Global Frequent Itemsets

Aiming at the problem that there exists prolific candidate sets and the communication cost is high for obtaining global frequent itemsets in distributed mining algorithms,an algorithm FDMA(fast distributed mining algorithm) for mining global frequent itemsets in distributed database is proposed.It improves the structure of frequent pattern tree(FP-tree) and the bidirectional FP-tree is transformed into unilateral one and only the pointer that points to the parent node is reserved at each node.The number of the pointer is decreased and one third of tree space can be saved.Meanwhile,by transmitting constrained sub-trees(consisting of three small arrays),the proposed algorithm doesn't generate conditional FP-tree or a large number of candidate sets in mining process,and thus the network traffic is reduced and the mining efficiency is increased.Experiments show that in comparison with DDDM(distributed dual decision miner) algorithm,the proposed algorithm increases the mining speed by at least two times.Moreover,the algorithm has better extensibility with increasing the scale of database.