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{{Missing information|something|date=February 2009}}
{{Original research|date=November 2016}}
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'''Vector quantization''' ('''VQ''') is a classical [[Quantization (signal processing)|quantization]] technique from [[signal processing]] that allows the modeling of [[probability density functions]] by the distribution of prototype vectors. It was originally used for [[data compression]]. It works by dividing a large set of points ([[coordinate vector|vector]]s) into groups having approximately the same number of points closest to them. Each group is represented by its [[centroid]] point, as in [[k-means]] and some other [[Cluster analysis|clustering]] algorithms. In simpler terms, vector quantization chooses a set of points to represent a larger set of points.
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