An initialization method for the k-means using the concept of useful nearest centers
The aim of the k-means is to minimize squared sum of Euclidean distance from the mean (SSEDM) of each cluster. The k-means can effectively optimize this function, but it is too sensitive for initial centers (seeds). This paper proposed a method for initialization of the k-means using the concept of...
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Veröffentlicht in: | arXiv.org 2017-05 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The aim of the k-means is to minimize squared sum of Euclidean distance from the mean (SSEDM) of each cluster. The k-means can effectively optimize this function, but it is too sensitive for initial centers (seeds). This paper proposed a method for initialization of the k-means using the concept of useful nearest center for each data point. |
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ISSN: | 2331-8422 |