PFPonCanTree:一种基于MapReduce的并行频繁模式增量挖掘算法  

PFPonCanTree:A parallel frequent patterns incremental mining algorithm based on MapReduce

作　　者：肖文[1] 胡娟[1] 周晓峰 

机构地区：[1]河海大学文天学院,安徽马鞍山243000

出　　处：《计算机工程与科学》2018年第1期15-23,共9页Computer Engineering & Science

基　　金：安徽省高校自然科学研究项目(KJ2016A623)

摘　　要：频繁模式挖掘是最重要的数据挖掘任务之一,传统的频繁模式挖掘算法是以"批处理"方式执行的,即一次性对所有数据进行挖掘,无法满足不断增长的大数据挖掘的需要。MapReduce是一种流行的并行计算模式,在并行数据挖掘领域已得到了广泛的应用。将传统频繁模式增量挖掘算法CanTree向MapReduce计算模型进行了迁移,实现了并行的频繁模式增量挖掘。实验结果表明,提出的算法实现了较好的负载均衡,执行效率有明显提升。Frequent pattern mining is one of the most important data mining tasks.Traditional frequent pattern mining algorithmsare executed in a " batch" mode,that is,all the data are mined in one time,so they cannotmeet the needs of the ever-growing bigdata mining.MapReduce is a popular parallel computing modeland has been widely used in the field of parallel data mining.In this paper,we migrate the traditional frequent pattern incremental mining algorithm CanTree to the MapReduce computing model,achieving aparallel frequent pattern incremental miningalgorithm.The experimental results show that the proposed algorithm achievesbetterload balancing and improvesthe execution efficiency significantly.

关 键 词：数据挖掘 频繁模式挖掘 增量挖掘 MAPREDUCE HADOOP PFP 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]