软件导刊Issue(4)：75-77,3.DOI:10.11907/rjdk.151007
一种基于 MapR educ e的频繁项集挖掘算法
A Algorithm for Mining Frequent Itemsets Based on MapReduce
孙兵率1
作者信息
- 1. 西安工程大学计算机科学学院，陕西西安710048
- 折叠
摘要
Abstract
With the arrival of the era of big data ,in view of the Apriori algorithm and FP‐Grow th algorithm in mining large scale frequent itemsets ,there exists some performance bottlenecks such as insufficient memory ,low calculation efficiency and so on .An improved Aggregating_FP algorithm is proposed ,the algorithm combines MapReduce with FP‐Growth algo‐rithm to realize the idea of parallel mining frequent itemsets .And in the output stage ,each item is processed by merging and the algorithm output only the first K frequent itemsets including the item to improve the effectiveness of mass data de‐cision value .M ultiple groups of different scale data sets are tested in Hadoop platform ,experimental results show that the Aggregating_FP algorithm is applied to analyze and deal with big data ,and has good expansibility .关键词
频繁项集/MapReduce/Hadoop/可扩展性Key words
Frequent Itemsets/MapReduce/Hadoop/Scalability分类
信息技术与安全科学引用本文复制引用
孙兵率..一种基于 MapR educ e的频繁项集挖掘算法[J].软件导刊,2015,(4):75-77,3.
