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

Abstract

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.

Keywords

• data clustering;large-scale data;spectral clustering;three-order tensor;clustering ensemble