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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2106.09852 |
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| _version_ | 1866917800932540416 |
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| author | Li, Hongmin Ye, Xiucai Imakura, Akira Sakurai, Tetsuya |
| author_facet | Li, Hongmin Ye, Xiucai Imakura, Akira Sakurai, Tetsuya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_09852 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | LSEC: Large-scale spectral ensemble clustering Li, Hongmin Ye, Xiucai Imakura, Akira Sakurai, Tetsuya Machine Learning 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. |
| title | LSEC: Large-scale spectral ensemble clustering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2106.09852 |