  In order to find previously unknown subgroups in biomedical data and generate
testable hypotheses, visually guided exploratory analysis can be of tremendous
importance. In this paper we propose a new dissimilarity measure that can be
used within the Multidimensional Scaling framework to obtain a joint
low-dimensional representation of both the samples and variables of a
multivariate data set, thereby providing an alternative to conventional
biplots. In comparison with biplots, the representations obtained by our
approach are particularly useful for exploratory analysis of data sets where
there are small groups of variables sharing unusually high or low values for a
small group of samples.
