CAJ | 학술논문

传统大数据填充算法是根据整个数据集对缺失数据进行填充，使得填充值容易受到不同类别数据的干扰，导致填充结果不精确。针对该问题，给出不完整数据的相似度度量方法，使用近邻传播( AP )算法对不完整数据进行聚类。采用云计算技术优化AP聚类算法，实现一种基于MapReduce的分布式聚类算法，根据算法聚类结果将同一类数据对象划分到相同簇中，并利用同一类对象的属性值对缺失值进行填充。实验结果表明，该算法能实现不完整大数据的聚类，同时加快聚类速度，提高缺失数据的填充精度。
전통대수거전충산법시근거정개수거집대결실수거진행전충，사득전충치용역수도불동유별수거적간우，도치전충결과불정학。침대해문제，급출불완정수거적상사도도량방법，사용근린전파( AP )산법대불완정수거진행취류。채용운계산기술우화AP취류산법，실현일충기우MapReduce적분포식취류산법，근거산법취류결과장동일류수거대상화분도상동족중，병이용동일류대상적속성치대결실치진행전충。실험결과표명，해산법능실현불완정대수거적취류，동시가쾌취류속도，제고결실수거적전충정도。
Traditional big data filling algorithms fill missing values depending on the statistical theory of the data set, and they are corrupted by noise data which decrease the imputation accuracy. This paper proposes an algorithm to fill missing values based on distributed incomplete big data clustering. It clusters incomplete big data directly by proposing a new similarity metrics,and uses cloud computing technology to improve clustering efficiency by designing MapReduce-based distributed Affinity Propagation( AP) clustering algorithm. The data in the same cluster is utilized to fill missing values. Experimental result demonstrates the proposed algorithm can cluster the incomplete big data directly and improve the filling accuracy of missing data effectively.