Papers › Divide-and-conquer based Large-Scale Spectral Clustering
Divide-and-conquer based Large-Scale Spectral Clustering
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image/Document Clustering | pendigits | LSC-R | Accuracy (%) | 81.55 | #2 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | LSC-R | NMI | 79.15 | #2 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | LSC-R | runtime (s) | 0.77 | #2 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | LSC-K | Accuracy (%) | 74.02 | #4 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | LSC-K | NMI | 81.37 | #4 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | LSC-K | runtime (s) | 1.20 | #4 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | U-SPEC | Accuracy (%) | 81.68 | #6 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | U-SPEC | NMI | 81.68 | #6 of 7 | Archive leaderboard | report |
| Image/Document Clustering | pendigits | U-SPEC | runtime (s) | 2.07 | #6 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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