Different dissimilarity measure will generate different representations. Various ways of combining them are possible. Some examples are shown here. It is assumed that readers are familiar with PRTools and will consult the following pages where needed:
If for the same set of objects a set of dissimilarity measures has been measured, the following options may be considered to combine them:
Below are some examples. In PRDisData the following datasets consist of a set of dissimilarity matrices of the same objects:
chickenpieces (44 sets),
covers_songs. Moreover, the separately mentioned datasets
coilyork refer to the same set of images in the coil database and
polydism57 are based on the same set of polygons.
elements: datasets datafiles cells and doubles mappings classifiers mapping types.
operations:datasets datafiles cells and doubles mappings classifiers stacked parallel sequential dyadic.
user commands:datasets representation classifiers evaluation clustering examples support routines.
introductory examples:IntroductionScatterplotsDatasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reductionCombining classifiers Dissimilarities.