Matrix constructions of centroid sets for classification systems

This article continues the investigation of matrix constructions motivated by
their applications to the design of classification systems. Our main
theorems strengthen and generalize previous results by describing all
centroid sets for classification systems that can be generated as one-sided
ideals with the largest weight in structural matrix semirings. Centroid sets
are well known in data mining, where they are used for the design of
centroid-based classification systems, as well as for the design of multiple
classification systems combining several individual classifiers.