Article

A Parametric K-Means Algorithm

Computational Statistics
Document Type

Article
Publication Date

1-1-2007
Disciplines

Abstract

The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points of a parametric distribution. Cluster means from the k-means algorithm are nonparametric estimators of principal points. A parametric k-means approach is introduced for estimating principal points by running the k-means algorithm on a very large simulated data set from a distribution whose parameters are estimated using maximum likelihood. Theoretical and simulation results are presented comparing the parametric k-means algorithm to the usual k-means algorithm and an example on determining sizes of gas masks is used to illustrate the parametric k-means algorithm.
DOI

10.1007/s00180-007-0022-7
Citation Information

Thaddeus Tarpey. "A Parametric K-Means Algorithm" *Computational Statistics*Vol. 22 Iss. 1 (2007) p. 71 - 89 ISSN: 0943-4062

Available at: http://works.bepress.com/thaddeus_tarpey/9/