### Mean shift clustering

Mean shift clustering is a general non-parametric cluster finding procedure — introduced by Fukunaga and Hostetler [1], and popular within the computer vision field. Nicely, and in contrast to the more-well-known K-means clustering algorithm, the output of mean shift does not depend on any explicit assumptions on the shape of the point distribution, the number of clusters, or any form of random initialization.

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