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Volume 19 (2022)
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Advances in Computer Science and Engineering
Advances in Computer Science and Engineering
Volume 1, Issue 1, Pages 61 - 72 (March 2007)
REFINING INITIAL POINTS FOR
K
-MEANS CLUSTERING
S. Y. Yang (P. R. China), D. H. Yuan (P. R. China) and G. M. Lai (P. R. China)
Abstract:
In this paper, we present two new methods to get a refined starting condition that is based on an efficient technique for estimating the modes of a distribution. We compare these two methods with other refining starting condition methods (such as Bradley’s refining, Chiu’s mountain, Kim’s kernel mountain and so on) through the applications of these methods to the popular
K
-means clustering algorithm and show that refined initial starting points got by our proposed methods indeed lead to improved solutions. The experiments demonstrate that these two improved
K
-means methods are more stable and more accurate when deal with massive data.
Keywords and phrases:
K-means, kernel
K
-means, mountain
K
-means, refining
K
-means, kernel-based refining
K
-means, kernel-based mountain
K
-means, kernel-based refining mountain
K
-means.
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P-ISSN: 0973-6999
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