Divide-and-conquer based Large-Scale Spectral Clustering

30 Apr 2021  ·  Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai ·

Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness. In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities. Then clustering results can be computed quickly through a bipartite graph partition process. The proposed method achieves a lower computational complexity than most existing large-scale spectral clustering methods. Experimental results on ten large-scale datasets have demonstrated the efficiency and effectiveness of the proposed method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li-Hongmin/MyPaperWithCode.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image/Document Clustering pendigits LSC-R Accuracy (%) 81.55 # 3
runtime (s) 0.77 # 2
NMI 79.15 # 4
Image/Document Clustering pendigits LSC-K Accuracy (%) 74.02 # 5
runtime (s) 1.20 # 4
NMI 81.37 # 3
Image/Document Clustering pendigits U-SPEC Accuracy (%) 81.68 # 2
runtime (s) 2.07 # 6
NMI 81.68 # 2

Methods