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Stochastic K-means algorithm for vector quantization
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DOI：10.1016/S0167-8655(01)00021-6.png)
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Abstract
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The proposed stochastic K-means algorithm (SKA) associates a vector with a cluster according to a probability distribution, which depends on the distance between the vector and the cluster gravity centre. It is less dependent than the K-means algorithm (KMA) on the initial centre choice. It can reach local minima closer to the global minimum of a distortion measure than the KMA. It has been applied to vector quantization of speech signals. (C) 2001 Elsevier Science B.V. All rights reserved.
Keywords:
K-means algorithm
stochastic methods
vector quantization
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