CAJ | 학술논문

协同聚类算法是通过同时对文档和特征进行聚类的一种聚类算法，该算法可以挖掘文档内部特征之间的潜在关系从而达到提高聚类效果的目的。随着大数据时代的到来，算法的并行化显示出它的优越性，为此本文对协同聚类算法进行全面的研究，并扩展它的并行算法，研究基于最小化残差平方和的协同聚类算法，利用MapReduce模式设计与实现协同聚类算法的并行化。实验结果表明，本文提出的并行协同聚类算法能够提高聚类的效率，并具有很好的可扩展性。
협동취류산법시통과동시대문당화특정진행취류적일충취류산법，해산법가이알굴문당내부특정지간적잠재관계종이체도제고취류효과적목적。수착대수거시대적도래，산법적병행화현시출타적우월성，위차본문대협동취류산법진행전면적연구，병확전타적병행산법，연구기우최소화잔차평방화적협동취류산법，이용MapReduce모식설계여실현협동취류산법적병행화。실험결과표명，본문제출적병행협동취류산법능구제고취류적효솔，병구유흔호적가확전성。
Collaborative clustering algorithm is a kind of clustering algorithm to cluster the documents and the features at the same time, this algorithm can find the potential relationship between internal document features so as to improve the clustering effect . With the arrival of the era of big data , parallel algorithm showed its superiority , this paper carries out a comprehensive research on collaborative clustering algorithm , and extends the parallel algorithm of it .We studied the collaborative clustering algorithm based on minimum sum-squared residue , and then designed and realized the parallel collaborative clustering algorithm with Ma -pReduce model .Experimental results show that the proposed parallel collaborative clustering algorithm can improve the efficiency of clustering , and be of well scalability .