CAJ | 학술논문

基于Hadoop分布式计算平台，给出一种适用于大数据集的并行挖掘算法。该算法对非结构化的原始大数据集以及中间结果文件进行垂直划分以确保能够获得完整的频繁项集，将各个垂直分块数据分配给不同的Hadoop计算节点进行处理，以减少各个计算节点的存储数据，进而减少各个计算节点执行交集操作的次数，提高并行挖掘效率。实验结果表明，给出的并行挖掘算法解决了大数据集挖掘过程中产生的大量数据通信、中间数据以及执行大量交集操作的问题，算法高效、可扩展。
기우Hadoop분포식계산평태，급출일충괄용우대수거집적병행알굴산법。해산법대비결구화적원시대수거집이급중간결과문건진행수직화분이학보능구획득완정적빈번항집，장각개수직분괴수거분배급불동적Hadoop계산절점진행처리，이감소각개계산절점적존저수거，진이감소각개계산절점집행교집조작적차수，제고병행알굴효솔。실험결과표명，급출적병행알굴산법해결료대수거집알굴과정중산생적대량수거통신、중간수거이급집행대량교집조작적문제，산법고효、가확전。
Based on Hadoop distributed computing framework,propose a parallel algorithm for mining the large dataset. The presented al-gorithm divides the original large non-structured dataset and large middle result files into several smaller-scale data blocks by vertical partitioning pattern in order to ensure the completeness of the frequent item set. The algorithm can reduce the size of the data to be stored in each computing node and decrease the execution times that each computing node calculates the intersection operations by distributing the data blocks to the computing nodes to parallel mining in Hadoop distributed computing environment,and it can improve the efficiency of parallel mining. The experimental results show that the presented parallel mining algorithm can solve the problem that the mining large dataset will generate large amount of data communication and large number of operations for calculating intersection,and it is efficient and scalable.