Compară metode

	Mean Shift	Clustering Ierarhic	Clustering K-means
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1975	1963	1967 (formalized 1982)
Autorul original	Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.	Ward, J. H.	MacQueen, J. B.; Lloyd, S. P.
Tip	Non-parametric mode-seeking / density-based clustering	Unsupervised clustering (agglomerative)	Partitional clustering
Sursa seminală	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Denumiri alternative	mean-shift clustering, mean shift mode seeking, kernel mean shift, nonparametric mode detection	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Înrudite	4	4	4
Rezumat	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	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.