Optimal variable weighting for ultrametric and additive trees and K-means partitioning: Methods and software

De Soete (1986, 1988) proposed some years ago a method for optimal variable weighting for ultrametric. and additive tree fitting. This paper extends D e Soete's method to optimal variable weighting for K-means partitioning. We also describe some new features and improvements to the algorithm proposed by De Soete. Monte Carlo simulations have been conducted using different e rror conditions. In all cases (i.e., ultrametric or additive trees, or K-me ans partitioning), the simulation results indicate that the optimal weighti ng procedure should be used for analyzing data containing noisy variables t hat do not contribute relevant information to the classification structure. However, if the data involve error-perturbed variables that are relevant t o the classification or outliers, it seems better to cluster or partition t he entities by using variables with equal weights, A new computer progam, O VW, which is available to researchers as freeware, implements improved algo rithms for optimal variable weighting for ultrametric and additive tree clu stering, and includes a new algorithm for optimal variable weighting for K- means partitioning.