Using the Potts glass for solving the clustering problem

We present an application of a Potts glass to the clustering problem. Simulated annealing in the mean field approximation is used in order to avoid local minima. The resulting updating equations are completely parallel, and very easy to implement. The model has no free parameters except for the annealing parameters. We show how the model can be implemented for some special clustering problems. The T-->0 limit of the Potts glass is identical to the vector quantization algorithm with certain increments. A comparative study of the Potts glass and vector quantization is also made, and it is shown that for difficult clustering problems, the Potts glass is far better than vector quantization.