Comparer des méthodes

	Regroupement flou par centroïdes (Fuzzy C-Means, FCM)	Spectral Clustering
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1981	2002
Auteur d'origine	Joseph Dunn; James Bezdek	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Soft (fuzzy) partitional clustering	Graph-based clustering (spectral method)
Source fondatrice	Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Alias	FCM, fuzzy clustering, soft k-means, bulanık c-ortalama kümeleme	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Apparentées	3	5
Résumé	Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.