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An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering

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2 Author(s)
N. B. Karayiannis ; Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA ; J. C. Bezdek

Derives an interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization. These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning. Derivation of the new algorithms is accomplished by minimizing an average generalized distance between the feature vectors and prototypes using gradient descent. A close relationship between the resulting algorithms and fuzzy c-means is revealed by investigating the functionals involved. It is also shown that the fuzzy c-means and fuzzy learning vector quantization algorithms are related to the proposed algorithms if the learning rate at each iteration is selected to satisfy a certain condition

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:5 ,  Issue: 4 )