Title :
Fully automatic clustering system
Author :
Patane, Giuseppe ; Russo, Marco
Author_Institution :
Dipt. di Fisica, Messina Univ., Italy
fDate :
11/1/2002 12:00:00 AM
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 toward the final solution.
Keywords :
convergence; neural nets; pattern clustering; unsupervised learning; vector quantisation; codebook; codewords; computations per iteration; convergence; fully automatic clustering system; greedy techniques; neural network; pattern clustering; unsupervised learning; vector quantization; Application software; Clustering algorithms; Computer vision; Image converters; Image segmentation; Magnetic analysis; Magnetic resonance imaging; Multidimensional systems; Unsupervised learning; Vector quantization;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.804226