Comparer des méthodes

	Mean Shift	Spectral Clustering
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1975	2002
Auteur d'origine	Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Non-parametric mode-seeking / density-based clustering	Graph-based clustering (spectral method)
Source fondatrice	Fukunaga, K. & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32–40.	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	mean-shift clustering, mean shift mode seeking, kernel mean shift, nonparametric mode detection	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Apparentées	4	5
Résumé	Mean Shift is a non-parametric, iterative mode-seeking algorithm that identifies clusters as the peaks of an underlying probability density function. Originally introduced by Fukunaga and Hostetler (1975) for gradient estimation in pattern recognition, it was substantially extended and popularized by Comaniciu and Meer (2002) for robust feature-space analysis and image segmentation. Unlike k-means, Mean Shift requires no prior specification of the number of clusters, deriving cluster structure entirely from the data density.	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.