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

Author

Karayiannis, Nicolaos B. ; Bezdek, James C.

Author_Institution

Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA

Volume

5

Issue

4

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

622

Lastpage

628

Abstract

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

Keywords

functional equations; fuzzy set theory; minimisation; neural nets; pattern recognition; unsupervised learning; vector quantisation; batch fuzzy learning vector quantization algorithms; competitive learning algorithms; competitive network; feature vectors; fuzzy c-means clustering; gradient descent; prototypes; unsupervised learning; Algorithm design and analysis; Clustering algorithms; Constraint optimization; Design optimization; Equations; Helium; Minimization methods; Prototypes; Unsupervised learning; Vector quantization;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.649915

Filename

649915