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Mean Shift, Mode Seeking, and Clustering
August 1995 (vol. 17 no. 8)
pp. 790-799

Abstract—Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization.

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Index Terms:
Mean shift, gradient descent, global optimization, Hough transform, cluster analysis, k-means clustering.
Citation:
Yizong Cheng, "Mean Shift, Mode Seeking, and Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799, Aug. 1995, doi:10.1109/34.400568
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