A new generalized learning vector quantization algorithm

Ching Tang Hsieh, Mu Chun Su, Uei Jyh Chen, Horng Jae Lee

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

A new approach of 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 be represented 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 the random initialization is effectiveness in detecting linear, spherical and ellipsoidal clusters. Besides, this method can also solve the crossed question.

Original languageEnglish
Pages339-344
Number of pages6
StatePublished - 2000
Event2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems - Tianjin, China
Duration: 4 Dec 20006 Dec 2000

Conference

Conference2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems
Country/TerritoryChina
CityTianjin
Period4/12/006/12/00

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