CAJ | 학술논문

对快速增长的数据进行挖掘的有效途径之一是采用增量式更新算法，其中最具代表性的是M RFU P算法。该算法的剪枝策略减少了关联规则的计算，但在处理增长快速的数据时效率过低，且频繁计算新增数据。文章以提高海量数据下关联规则增量更新效率为目标，通过扩展能够并行处理关联规则的PFP算法而提出一种基于PFP的关联规则增量更新算法M RPFP。该算法能充分利用云平台强大的存储和并行计算能力。该算法的实验结果表明，M RPFP处理海量数据的效率优于 M RFU P算法，更适用于海量数据的关联规则挖掘。
대쾌속증장적수거진행알굴적유효도경지일시채용증량식경신산법，기중최구대표성적시M RFU P산법。해산법적전지책략감소료관련규칙적계산，단재처리증장쾌속적수거시효솔과저，차빈번계산신증수거。문장이제고해량수거하관련규칙증량경신효솔위목표，통과확전능구병행처리관련규칙적PFP산법이제출일충기우PFP적관련규칙증량경신산법M RPFP。해산법능충분이용운평태강대적존저화병행계산능력。해산법적실험결과표명，M RPFP처리해량수거적효솔우우 M RFU P산법，경괄용우해량수거적관련규칙알굴。
One effective way for the rapidly growing data mining is the incremental updating algorithm ,which is represented by the MRFUP algorithm .MRFUP algorithm has a good advantage in the maintenance of asso‐ciation rules with its pruning strategy ,but it has low efficiency in the rapidly growing data processing and cal‐culates the new data frequently .In this paper ,aiming at improving the efficiency of association rules incre‐mental updating of the massive data ,an association rules incremental updating algorithm M RPFP is proposed by extending the parallel processing algorithm of association rules PFP .The algorithm can take advantage of powerful cloud storage and parallel computing capabilities .The experimental results show that MRPFP is more efficient in processing massive data than MRFUP and more suitable for the association rules mining of massive data .