Abstract
In fuzzy clustering ensemble, the quality of fuzzy base clustering has an important influence on the performance of the final clustering result. Due to the performance of fuzzy clustering is affected by the initial parameters and fuzzy factors, it may cause unstable clustering results such as unsatisfactory data affiliation, and large differences with the distribution of the real data set. In addition, how to use fuzzy ensemble information to determine the similarity among samples effectively plays a crucial role in the generation of co-association matrix elements. In view of the above problems, combined with the compactness, separation and overlap in the evaluation index of fuzzy clustering, an optimized fuzzy clustering evaluation index is designed to select high quality fuzzy base clustering members to participate the final fusion. Then, the concept of sample attribution clarity is proposed, and the attribution clarity of each sample in the fuzzy base clustering set is learned actively. For samples with different attribution clarity, different full-link similarity measurement methods between samples are designed to further reduce the uncertainty of samples. Finally, the clustering results are obtained by the agglomerative hierarchical clustering. In order to verify the effectiveness of the proposed method, ten data sets are used to conduct experiments. Experiments show that the results obtained by the proposed method are closer to the real distribution structure of the data set in most experimental dataset, and are not sensitive to the diversity of base clustering members, and have good robustness.
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The experimental data sets in this paper are all from the UCI Machine Learning repository.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No.62206240), the Shandong Provincial Natural Science Foundation of China (No. ZR2020QF110), and Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (GUANGXI MINZU UNIVERSITY)(No. GXIC20-04).
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Xu, L., Yan, X., Huang, J. et al. A fuzzy clustering ensemble selection based on active full-link similarity. Int. J. Mach. Learn. & Cyber. 14, 4325–4337 (2023). https://doi.org/10.1007/s13042-023-01896-5
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DOI: https://doi.org/10.1007/s13042-023-01896-5