Spectral clustering ensemble algorithm based on three-order tensor for large-scale data
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.
data clusteringlarge-scale dataspectral clusteringthree-order tensorclustering ensemble
万方数据
维普
