作　　者： (何梦娇）; (杨燕）; (王淑营）;

机构地区： 西南交通大学信息科学与技术学院,成都610031

出　　处： 《计算机科学》 2017年第9期58-61,共4页

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。 A NMF-based K-means clustering ensemble （NBKCE） algorithm was proposed for solving the problem of effective information loss in ensemble, which is caused by basic clustering results obtained from the original datasets. In NBKCE,an ensemble information matrix is built primarily by exploiting the results of the K-means, and then the rela- tionship matrix is formed based on the original dataset. At last nonnegative matrix factorization （NMF） is employed to construct consensus function to gain the final results. The experiments demonstrate that the NBKCE may attain the un- derlying information effectively and improve the perfornmnce of the clustering.

关 键 词： 聚类集成 潜在信息