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Proceedings Paper

A genetic clustering algorithm for searching nonspherically shaped clusters
Author(s): Shiueng Bien Yang; Yi L. Lee
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Paper Abstract

The K-means algorithm is a well-known method for searching the clustering. However, the K-means algorithm is suitable to find the clustering that contains compact spherical clusters. If the shape of clusters is not spherical, the K-means algorithm is failure to find the clustering result. Therefore, in this study, the genetic clustering algorithm is proposed to find the clustering whether the shape of clusters is spherical or not. Also, the genetic clustering algorithm can automatically find the number of clusters in the data set. Thus, the users need not to pre-dine the number of clusters in the data set. Experimental results show our proposed genetic clustering algorithm achieves better performance than the traditional clustering algorithms.

Paper Details

Date Published: 1 May 2003
PDF: 5 pages
Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515165
Show Author Affiliations
Shiueng Bien Yang, Leader Univ. (Taiwan)
Yi L. Lee, Leader Univ. (Taiwan)


Published in SPIE Proceedings Vol. 5132:
Sixth International Conference on Quality Control by Artificial Vision
Kenneth W. Tobin; Fabrice Meriaudeau, Editor(s)

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