Abstract
The fuzzy C-means (FCM) algorithm is a classical clustering algorithm which is widely used. However, especially for high-dimensional data sets with complex structures, the large-scale calculation of FCM suffers from decreasing clustering effect. In order to improve the clustering performance, we propose two new modified fuzzy clustering algorithms—modified fuzzy clustering algorithm based on non-negative matrix factorization (MFCM-NMF) and modified fuzzy clustering algorithm based on non-negative matrix factorization with local constraint (MFCM-LCNMF). Since MFCM-NMF combines NMF with modified FCM, the algorithm can use the dimensionality reduction technology of NMF, which greatly improves the computational efficiency. MFCM-LCNMF introduces NMF with local linear constraints into modified FCM, and it has a new objective function and adopts a new algorithm for alternate iteration. In the iterative process, the new membership update formula is utilized for the samples selected by the triangle inequality, which not only reduces the amount of calculation, but also obtains a higher clustering quality. Finally, a number of experiments on many data sets verify that MFCM-NMF and MFCM-LCNMF are more effective compared with other state-of-the-art methods.
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Data availability
The datasets analyzed during this study are available in [UC Irvine Machine Learing Repository, The Extended Yale Face Database B]. These datasets come from the following address: [https://archive.ics.uci.edu/ml/index.php, https://computervisiononline.com/dataset/1105138686].
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (11961010, 61967004).
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Li, X., Fan, X. & Lu, X. Modified fuzzy clustering algorithm based on non-negative matrix factorization locally constrained. J Ambient Intell Human Comput 14, 11373–11383 (2023). https://doi.org/10.1007/s12652-023-04651-4
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DOI: https://doi.org/10.1007/s12652-023-04651-4