CAJ | 학술논문

MapReduce是一种编程模型，可以运行在异构环境下，编程简单，不必关心底层实现细节，用于大规模数据集的并行运算。将MapReduce应用在数据挖掘的三个算法中：朴素贝叶斯分类算法、K-modes聚类算法和ECLAT频繁项集挖掘算法。实验结果表明，在保证算法准确率的前提下，MapReduce可以有效提高海量数据挖掘工作的效率。
MapReduce시일충편정모형，가이운행재이구배경하，편정간단，불필관심저층실현세절，용우대규모수거집적병행운산。장MapReduce응용재수거알굴적삼개산법중：박소패협사분류산법、K-modes취류산법화ECLAT빈번항집알굴산법。실험결과표명，재보증산법준학솔적전제하，MapReduce가이유효제고해량수거알굴공작적효솔。
MapReduce is a programming model which can run in a heterogeneous environment for mining massive volume of data. It is simple to be implemented without paying attention to the underlying details and can be used for large-scale parallel computing. In this paper, three data mining algorithms, Naive Bayes, K-modes, ECLAT are implemented by employing the MapReduce programming model. The results indicate that MapReduce can perform the data mining tasks on massive volume of data efficiently.