Ellipsoidal <i>K</i>-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions

Traditional <i>K</i>-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to...

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Auteurs principaux:	Alaa E. Abdel-Hakim, Abdel-Monem M. Ibrahim, Kheir Eddine Bouazza, Wael Deabes, Abdel-Rahman Hedar
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2024-12-01
Collection:	Algorithms
Sujets:	unsupervised learning data clustering simulated annealing silhouette function adaptive ellipsoidal <i>K</i>-means automatic clustering
Accès en ligne:	https://www.mdpi.com/1999-4893/17/12/551
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