Porovnať metódy

	Mean Shift	DBSCAN
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	1975	1996
Tvorca	Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Typ	Non-parametric mode-seeking / density-based clustering	Density-based clustering algorithm
Pôvodný zdroj	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.
Ďalšie názvy	mean-shift clustering, mean shift mode seeking, kernel mean shift, nonparametric mode detection	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Príbuzné	4	3
Zhrnutie	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.