• multivariate analysis;
  • unsupervised classification;
  • principal component analysis;
  • principal component similarity analysis;
  • heuristic cluster analysis;
  • model-based cluster analysis


This paper reviews three unsupervised multivariate classification methods: principal component analysis, principal component similarity analysis and heuristic cluster analysis. The theoretical basis of each method is presented in brief, and assumptions inherent to the methods are highlighted. A literature review shows that these methods have sometimes been used inappropriately or without referencing all essential parameters. The paper also brings to the attention of the reader a relatively unknown method: probabilistic or model-based cluster analysis. The goal of this method is to uncover the true classification of objects rather than a convenient classification provided by the other methods. For this reason it is felt that model-based cluster analysis will have broad application in the future. Copyright © 2008 Society of Chemical Industry