Módszerek összehasonlítása

	Mean Shift	K-means klaszterezés	Spektrális klaszterezés
Tudományterület	Gépi tanulás	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	1975	1967 (formalized 1982)	2002
Megalkotó	Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.	MacQueen, J. B.; Lloyd, S. P.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Típus	Non-parametric mode-seeking / density-based clustering	Partitional clustering	Graph-based clustering (spectral method)
Alapmű	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	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.
Alternatív nevek	mean-shift clustering, mean shift mode seeking, kernel mean shift, nonparametric mode detection	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Kapcsolódó	4	4	5
Összefoglaló	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.