Editing Vector quantization (section)

=== Use as clustering algorithm ===
As VQ is seeking for centroids as density points of nearby lying samples, it can be also directly used as a prototype-based clustering method: each centroid is then associated with one prototype. 
By aiming to minimize the expected squared quantization error<ref>{{cite journal|last=Gray|first=R.M.|title=Vector Quantization|journal=IEEE ASSP Magazine|year=1984|volume=1|issue=2|pages=4–29|doi=10.1109/massp.1984.1162229}}</ref> and introducing a decreasing learning gain fulfilling the Robbins-Monro conditions, multiple iterations over the whole data set with a concrete but fixed number of prototypes converges to the solution of [[k-means]] clustering algorithm in an incremental manner.