关联规则挖掘的DCOM实现

数据挖掘中一个重要的问题是从事物数据库中发现关联规则,其中最耗时的操作是候选项集出现频率的计算.实现的并行算法基于Apriori算法的Count Distribution Algorithm基本思想,在机群环境下用DCOM方式实现求解频繁项集的并行算法.实验结果表明,该并行算法能充分利用各节点机上CPU的资源,提高程序的运行效率,利用DCOM技术能够方便、有效地实现并行算法.

DCOM implementation of association rules mining

One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the occurrence frequency computation of interesting subset of items in the database of transactions. The parallel algorithm realized in this paper is based on count distribution algorithm using PC-clusters. The experiment results show that this parallel algorithm can take advantage of resources in node machines, improve running efficiency of program. Using DCOM technology can realize parallel algorithm conveniently and effectively.