方法对比

	均值漂移	DBSCAN	K-means聚类
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	1975	1996	1967 (formalized 1982)
提出者	Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.
类型	Non-parametric mode-seeking / density-based clustering	Density-based clustering algorithm	Partitional clustering
开创性文献	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
别名	mean-shift clustering, mean shift mode seeking, kernel mean shift, nonparametric mode detection	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
相关	4	3	4
摘要	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.	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.