A Parametric K-Means AlgorithmComputational Statistics
AbstractThe 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.
Citation InformationThaddeus 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/