Species Clustering via Classical and Interval Data Representation
Résumé
Consider a data table where n objects are described by p numerical variables and a qualitative variable with m categories. Interval data representation and interval data clustering methods are useful for clustering the m categories. We study in this paper a data set of fish contaminated with mercury. We will see how classical or interval data representation can be used for clustering the species of fish and not the fish themselves. We will compare the results obtained with the two approaches (classical or interval) in the particular case of this application in Ecotoxicology.