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K-means clustering is a method for identifying groups of similar observations, i.e. cells or samples. K-means clustering aims to group observations into a pre-determined number of clusters (k) so that each observation belongs to the cluster with the nearest mean. An important limitation aspect of K-means clustering is that it expects clusters to be of similar size (equal variance) and shape (distribution of variance is spherical). 

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