Multiclus: A New Method for Simultaneously Performing Multidimensional Scaling and Cluster Analysis
Psychometrika, Volume 56, Issue 1, pp 121-136
16 Pages Posted: 1 Jun 2016
Date Written: March 1991
This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K vectors, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood method is introduced together with an E-M algorithm for parameter estimation. A Monte Carlo analysis is presented to investigate the performance of the algorithm as a number of data, parameter, and error factors are experimentally manipulated. Finally, a consumer psychology application is discussed involving consumer expertise/experience with microcomputers.
Keywords: multidimensional scaling, cluster analysis, maximum likelihood estimation, consumer psychology
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