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arXiv · 2106.09852

LSEC: Large-scale spectral ensemble clustering

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

Ensemble clustering is a fundamental problem in the machine learning field, combining multiple base clusterings into a better clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks due to the efficiency bottleneck. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to strike a good balance between efficiency and effectiveness. In LSEC, a large-scale spectral clustering based efficient ensemble generation framework is designed to generate various base clusterings within a low computational complexity. Then all based clustering are combined through a bipartite graph partition based consensus function into a better consensus clustering result. The LSEC method achieves a lower computational complexity than most existing ensemble clustering methods. Experiments conducted on ten large-scale datasets show the efficiency and effectiveness of the LSEC 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. 2021-06-18. LSEC: Large-scale spectral ensemble clustering. https://doi.org/10.3233/ida-216240

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