CAJ | 학술논문

为了改进关联规则挖掘的经典Apriori算法,设计一种基于Map/Reduce的频繁项集挖掘方法。通过搭建Hadoop平台,可使该方法得以实现,并籍此对该方法与Apriori算法的性能进行比较研究。实验结果表明该方法在对大数据集进行频繁项集挖掘时,可充分利用云计算的优势,从而能获得更好的时效性。
위료개진관련규칙알굴적경전Apriori산법,설계일충기우Map/Reduce적빈번항집알굴방법。통과탑건Hadoop평태,가사해방법득이실현,병적차대해방법여Apriori산법적성능진행비교연구。실험결과표명해방법재대대수거집진행빈번항집알굴시,가충분이용운계산적우세,종이능획득경호적시효성。
To improve the classic Apriori algorithm of the associate rules mining, a method for frequent set mining based on MapReduce is propose.d. The new method can be implemented based on a platform of Hadoop, and thus, an experimental research can be given to compare the performance of the new method with that of the Apriori. The result shows that, the new method can take advantages of cloud computing to get good efficiency when mining mass data.