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
1. Verfasser:	Ismkhan, Hassan
Format:	Artikel
Sprache:	eng
Schlagworte:	Data points Euclidean geometry Seeds
Online-Zugang:	Volltext
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