Fully Automatic Clustering System

Giuseppe Patanè and Marco Russo

Abstract

In this paper the Fully Automatic Clustering System (FACS) is presented. It is a technique for clustering and vector quantization whose objective is the automatic calculation of the codebook of the right dimension, the desired error (or target) being fixed. At each iteration, FACS tries to improve the setting of the existing codewords and, if necessary, some elements are removed from or added to the codebook. In order to save on the number of computations per iteration, greedy techniques are adopted. It has been demonstrated, from a heuristic point of view, that the number of the codewords determined by FACS is very low and that the algorithm quickly converges towards the final solution.

Keywords: Clustering, Unsupervised Learning, Vector Quantization, FACS, ELBG.

[MyFACS] G. Patanè and M. Russo, Fully Automatic Clustering System. IEEE Transactions on Neural Networks, vol. 13 no. 6, pp 1285-1298, November 2002.

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