Our concern here, is the characterization of dissimilarity
indexes defined over finite sets, whose spatial representation is
spherical. Consequently, we propose a methodology (Normed
MultiDimensional
Scaling) to determine the spherical euclidean representation of a set of
items
best accounting for the initial dissimilarity between items. This
methodology
has the advantage of being graphically readable on individual qualities
of
projection like the normed PCA, of which it constitutes a
generalization. Moreover, it avoids the arbitrary character of spherical
encoding which the use of similitude functions currently used in MDS,
implies.