基于三阶张量的大规模数据谱聚类集成算法

Spectral clustering ensemble algorithm based on three-order tensor for large-scale data

为了降低大规模数据谱聚类计算负担,进一步提高聚类的准确性和鲁棒性,提出了一种基于三阶张量的大规模数据谱聚类集成算法。首先,提出一种混合代表最近邻近似方法构造数据间的稀疏亲和子矩阵;然后将稀疏亲和子矩阵表示为二部图,通过图分割的方法得到初步聚类结果;最后,提出三阶张量集成方法,将多个聚类结果进行融合,得到最终的聚类结果。在大规模的真实数据集和合成数据集上验证,相较经典的谱聚类算法、聚类集成算法以及近年来对其改进的算法,该算法表现出更优异的性能。

In order to reduce the computational burden of large-scale data spectral clustering and further improve the clustering accuracy and robustness,the spectral clustering ensemble algorithm based on the three-order tensor for large-scale data was proposed.The sparse affinity sub-matrix was first constructed by the mixed representative nearest neighbor approximation method.The sparse affinity sub-matrix was then represented as a bipartite graph.The preliminary clustering results were obtained by Graph Segmentation.Finally,an unified clustering result was obtained by fusing multiple clustering results through the three-order tensor ensemble method.On the real datasets and the synthetic datasets,the proposed algorithm showed a better performance compared to the classical spectral clustering algorithm,the clustering ensemble algorithm,and the improved algorithms in recent years.