K-Means algorithm based on multi-feature-induced order-Reference-Cited by-同舟云学术

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K-Means algorithm based on multi-feature-induced order

Published:2024-04-09 Issue:2 Volume:9 Page:
ISSN:2364-4966
Container-title:Granular Computing
language:en
Short-container-title:Granul. Comput.

Author:

Wan Benting,Huang Weikang,Pierre Bilivogui,Cheng Youyu,Zhou Shufen

Funder

Humanities and Social Science Planning Project of the Ministry of Education

Science and Technology Project of Jiangxi Provincial Education Department

18th Student Research Project of Jiangxi University of Finance and Economics

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41066-024-00470-w.pdf

Reference57 articles.

1. Abbaszadeh Shahri A, Shan C, Larsson S (2022) A novel approach to uncertainty quantification in groundwater table modeling by automated predictive deep learning. Nat Resour Res 31:1351–1373. https://doi.org/10.1007/s11053-022-10051-w

2. Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, pp 1027–1035

3. Asheghi R, Hosseini SA, Saneie M, Shahri AA (2020) Updating the neural network sediment load models using different sensitivity analysis methods: a regional application. J Hydroinf 22:562–577. https://doi.org/10.2166/hydro.2020.098

4. Askari S (2021) Noise-resistant fuzzy clustering algorithm. Granul Comput 6:815–828. https://doi.org/10.1007/s41066-020-00230-6

5. Ay M, Özbakır L, Kulluk S, Gülmez B, Öztürk G, Özer S (2023) FC-Kmeans: fixed-centered K-means algorithm. Expert Syst Appl 211:118656. https://doi.org/10.1016/j.eswa.2022.118656

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