Abstract
Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms.
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Acknowledgements
This work was supported in part by National Science Foundation of China (No. U1736105) and also supported by the National Social Science Foundation of China (No. 16ZDA054). The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RGP-264.
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Ma, T., Yu, T., Wu, X. et al. Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble. Soft Comput 24, 15129–15141 (2020). https://doi.org/10.1007/s00500-020-05264-1
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DOI: https://doi.org/10.1007/s00500-020-05264-1