A global algorithm for clustering univariate observations
摘要
This paper deals with the clustering of univariate observations: given a set of observations coming from possible clusters, one has to estimate the cluster means. We propose an algorithm based on the minimization of the "KP" criterion we introduced in a previous work. In this paper, we show that the global minimum of this criterion can be reached by first solving a linear system then calculating the roots of some polynomial of order . The KP global minimum provides a first raw estimate of the cluster means, and a final clustering step enables to recover the cluster means. Our method's relevance and superiority to the Expectation-Maximization algorithm is illustrated through simulations of various Gaussian mixtures. \keywords{unsupervised clustering \and non-iterative algorithm \and optimization criterion \and univariate observations
引用
@article{arxiv.physics/0703281,
title = {A global algorithm for clustering univariate observations},
author = {Paul Terre Fety},
journal= {arXiv preprint arXiv:physics/0703281},
year = {2007}
}