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.
|Number of pages||6|
|State||Published - 2000|
|Event||2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems - Tianjin, China|
Duration: 4 Dec 2000 → 6 Dec 2000
|Conference||2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems|
|Period||4/12/00 → 6/12/00|