k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid). This results in a partitioning of the data space into Voronoi cells.
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid). This results in a partitioning of the data space into Voronoi cells.
Source: Wikipedia "K-means clustering" · CC BY-SA 4.0
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