计算机应用与软件2016，Vol.33Issue(9)：20-23,162,5.DOI:10.3969/j.issn.1000-386x.2016.09.005
不确定数据流中频繁模式的并行挖掘算法
A PARALLEL MINING ALGORITHM WITH FREQUENT PATTERN FOR UNCERTAIN DATA STREAM
摘要
Abstract
One of the research focuses of frequent pattern mining in uncertain dataset is to improve time and space efficiency of the mining algorithm,especially in the case of growing data amount increase at present,the practical applications have higher demand on the efficiency of mining algorithms as well.Aiming at the frequent pattern mining model for dynamic uncertain data streams,we propose a MapReduce-based parallel mining algorithm on the basis of the algorithm of AT-Mine.By invoking twice at most the MapReduce procedures this algorithm can mine all the frequent patterns from a sliding window.In experiments presented in the paper,in majority cases by only executing MapReduce once it is able mine all frequent itemset,and the stream data can be distributed uniformly to each node according to the size of their amount. Experiments validate that the proposed algorithm can raise the time efficiency one order of magnitude.关键词
不确定数据/频繁模式/数据挖掘/并行算法Key words
Uncertain data/Frequent pattern/Data mining/Parallel algorithm分类
信息技术与安全科学引用本文复制引用
常艳芬,王乐,王辉兵..不确定数据流中频繁模式的并行挖掘算法[J].计算机应用与软件,2016,33(9):20-23,162,5.基金项目
国家自然科学基金项目（61370200）；宁波市自然科学基金项目（2013A610115，2014A610073）；宁波市软科学研究计划项目（2014A10008）；浙江省科技厅计划项目（2016C31128）；浙江省教育厅一般科研项目（Y201533234）。 （）
