General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gravitational Fuzzy Clustering
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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Fuzzy learning vector quantization (FLVQ), also known as the fuzzy Kohonen clustering network, was developed to improve performance and usability of online hard-competitive Kohonen VQ and soft-competitive self-organizing map (SOM) algorithms. FLVQ's effectiveness seems to depend on the range of change of the weighting exponent m(t). Extreme m(t) values (1 and ∞) are employed to investigate FLVQ asymptotic behaviors. This analysis shows that when m(t) tends to either extreme, FLVQ is affected by trivial VQ, which causes centroids to collapse in the grand mean of the input data set. No analytical criterion has been found to improve the heuristic choice of the range of m(t) change. Two FLVQ and SOM classification experiments of remote sensed data are then presented. The two nets are connected in cascade to a supervised second stage, based on the delta rule. The results confirm that FLVQ performance can be greatly affected by the user's definition of the range of change of the weighting exponent. Moreover, FLVQ shows instability when its traditional termination criterion is applied. Empirical recommendations are proposed for the enhancement of FLVQ robustness. Both the analytical and the experimental data reported seem to indicate that the choice of the range of m(t) change is still open to discussion and that alternative clustering neural-network approaches should be developed to pursue during training: (1) monotone reduction of the neurons' learning rate and (2) monotone reduction of the overlap among neuron receptive fields