An initialization method for the k-means using the concept of useful nearest centers
Machine Learning
2017-05-11 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1705.03613,
title = {An initialization method for the k-means using the concept of useful nearest centers},
author = {Hassan Ismkhan},
journal= {arXiv preprint arXiv:1705.03613},
year = {2017}
}