CAJ | 학술논문

大数据处理是物联网研究和应用上不可回避的难题之一，针对常用聚类方法在大数据处理上的不足，设计了一种划分聚类新方法。该方法采用了大数据集的抽样技术，对多次抽取的规模足够大的样本进行聚类以确定自然簇质心的初始位置，在此基础上采用抽样后剩余数据样本对质心的初始位置进行更新，以便校正偏离理想位置的初始质心。该划分聚类算法具有线性空间复杂度和时间复杂度。实验结果表明所提的新聚类算法不仅能得到比常用聚类算法更理想的结果，而且运行速度快，适合处理大规模数据的聚类任务。
대수거처리시물련망연구화응용상불가회피적난제지일，침대상용취류방법재대수거처리상적불족，설계료일충화분취류신방법。해방법채용료대수거집적추양기술，대다차추취적규모족구대적양본진행취류이학정자연족질심적초시위치，재차기출상채용추양후잉여수거양본대질심적초시위치진행경신，이편교정편리이상위치적초시질심。해화분취류산법구유선성공간복잡도화시간복잡도。실험결과표명소제적신취류산법불부능득도비상용취류산법경이상적결과，이차운행속도쾌，괄합처리대규모수거적취류임무。
Large data processing is an inevitable problem for the internet of things research and application. To solve the shortcomings of large data processing with the common clustering methods,a novel partitional clustering method is designed.The new method determines the initial positions of natural cluster centroids by clustering the samples in sizes large enough,which are selected using the large data sampling method repeated-ly.Next it updates the initial positions using the remaining data to correct the centroids positions deviating from the ideal positions.The designed partitional clustering algorithm has linear space and time complexity.The ex-perimental results show that this new clustering algorithm can not only give better clustering results than com-mon clustering algorithms,but also run fast and be suitable for large data clustering processing.