Title :
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
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;
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
DOI :
10.1109/APCCAS.2000.913504
