  Standard multidimensional scaling takes as input a dissimilarity matrix of
general term $\delta _{ij}$ which is a numerical value. In this paper we input
$\delta _{ij}=[\underline{\delta _{ij}},\overline{\delta _{ij}}]$ where
$\underline{\delta _{ij}}$ and $\overline{\delta _{ij}}$ are the lower bound
and the upper bound of the ``dissimilarity'' between the stimulus/object $S_i$
and the stimulus/object $S_j$ respectively. As output instead of representing
each stimulus/object on a factorial plane by a point, as in other
multidimensional scaling methods, in the proposed method each stimulus/object
is visualized by a rectangle, in order to represent dissimilarity variation. We
generalize the classical scaling method looking for a method that produces
results similar to those obtained by Tops Principal Components Analysis. Two
examples are presented to illustrate the effectiveness of the proposed method.
