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arXiv · 2104.15042

Divide-and-conquer based Large-Scale Spectral Clustering

Hongmin Li·Xiucai Ye·Akira Imakura·Tetsuya Sakurai
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Abstract

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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Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai. 2021-04-30. Divide-and-conquer based Large-Scale Spectral Clustering. https://doi.org/10.1016/j.neucom.2022.06.006

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