An initialization method for the k-means using the concept of useful nearest centers

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
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Data points
Euclidean geometry
Seeds
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description 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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Seeds
title An initialization method for the k-means using the concept of useful nearest centers
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