Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis

Wayne S. DeSarbo, Daniel J. Howard, Kamel Jedidi

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)121-136
Number of pages16
JournalPsychometrika
Volume56
Issue number1
DOIs
StatePublished - Mar 1991

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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