A global algorithm for clustering univariate observations
| dc.creator | Fety, Paul Terre | |
| dc.date | 2007-03-30 | |
| dc.date.accessioned | 2026-07-07T07:55:14Z | |
| dc.date.available | 2026-07-07T07:55:14Z | |
| dc.description | This paper deals with the clustering of univariate observations: given a set of observations coming from $K$ 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 $K$. 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 | |
| dc.identifier | https://arxiv.org/abs/physics/0703281 | |
| dc.identifier | http://arxiv.org/abs/physics/0703281 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/126894 | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.title | A global algorithm for clustering univariate observations | |
| dc.type | text |