Fully automatic clustering system

Authors:
G. Patane;M. Russo
Affiliations:
Dipt. di Fisica, Messina Univ.;-
Venue:
IEEE Transactions on Neural Networks
Year:
2002

Citing 0
Cited 4

Variations of the two-spiral task

Connection Science
An efficient algorithm for parallel distributed unsupervised learning

Neurocomputing
Vector Quantization of Images Using a Fuzzy Clustering Method

Cybernetics and Systems
An algorithm for vector quantization using density estimation

SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing

Quantified Score

Hi-index	0.00

Visualization

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.