k-means: a new generalized k-means clustering algorithm

Authors:
Yiu-Ming Cheung
Affiliations:
Department of Computer Science, Hong Kong Baptist University, 7/F Sir Run Run Shaw Building, Kowloon Tong, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

This paper presents a generalized version of the conventional k-means clustering algorithm [Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, University of California Press, Berkeley, 1967, p. 281]. Not only is this new one applicable to ellipse-shaped data clusters without dead-unit problem, but also performs correct clustering without pre-assigning the exact cluster number. We qualitatively analyze its underlying mechanism, and show its outstanding performance through the experiments.