A new generalized learning vector quantization algorithm

Author

Hsieh, Ching-Tang ; Su, Mu-Chun ; Chen, Uei-Jyh ; Lee, Homg-Jae

Author_Institution

Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan

fYear

2000

fDate

2000

Firstpage

339

Lastpage

344

Abstract

A new approach to data clustering which is capable of detecting clusters of different shapes is proposed. In classical clustering approaches, it is a great challenge to separate clusters if the cluster prototypes are difficult to represent by a mathematical formula. In this paper, we propose an improved learning vector quantization (LVQ) algorithm using the concept of symmetry. Through several computer simulations, the results show that the proposed method with random initialization is effective in detecting linear, spherical and ellipsoidal clusters. Besides, this method can also solve the crossed question

Keywords

data handling; learning (artificial intelligence); neural nets; pattern clustering; symmetry; vector quantisation; cluster shape detection; data clustering; ellipsoidal clusters; generalized learning VQ algorithm; linear clusters; pattern recognition; random initialization; spherical clusters; symmetry; vector quantization algorithm; Clustering algorithms; Computer simulation; Euclidean distance; Loss measurement; Neural networks; Prototypes; Shape; Supervised learning; Vector quantization; Weight measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on

Conference_Location

Tianjin

Print_ISBN

0-7803-6253-5

Type

conf

DOI

10.1109/APCCAS.2000.913504

Filename

913504