A Fuzzy Granular K-Means Clustering Method Driven by Gaussian Membership Functions
The K-means clustering algorithm is widely applied in various clustering tasks due to its high computational efficiency and simple implementation. However, its performance significantly deteriorates when dealing with non-convex structures, fuzzy boundaries, or noisy data, as it relies on the assumpt...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI AG
2026-01-01
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| coleção: | Mathematics |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2227-7390/14/3/462 |
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