计算机科学 ›› 2017, Vol. 44 ›› Issue (9): 58-61.doi: 10.11896/j.issn.1002-137X.2017.09.011
• CRSSC-CWI-CGrC 2016 • 上一篇 下一篇
何梦娇,杨燕,王淑营
HE Meng-jiao, YANG Yan and WANG Shu-ying
摘要: 为了解决通过原始数据集获得的基聚类结果存在一定的信息丢失,从而使得集成阶段的有效信息减少的问题,提出了一种基于非负矩阵分解的K-means聚类集成算法。该算法先利用K-means聚类算法获得集成信息矩阵,然后从原始数据集获取数据相关性,将两者结合后通过非负矩阵分解(NMF)技术构建共识函数以获得最终结果。实验证明,所提算法可以有效获取原始数据的潜在信息,并提高聚类质量。
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