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A class-conditional hierarchical clustering framework has been used to generalize and improve previously proposed condensing schemes to obtain multiple prototype classifiers. The proposed method conveniently uses geometric properties and clusters to efficiently obtain reduced sets of prototypes that accurately represent the data while significantly keeping its discriminating power. The benefits of the proposed approach are empirically assessed with regard to other previously proposed algorithms which are similar in their foundations. Other well-known multiple prototype classifiers have also been taken into account in the comparison.