Combining Dissimilarities for Three-Way Data Classification

Abstract

Abstract. The representation of objects by multidimensional arrays is widely applied in many research areas. Nevertheless, there is a lack of tools to classify data with this structure. In this paper, an approach for classifying objects represented by matrices is introduced, based on the advantages and success of the combination strategy, and particularly in the dissimilarity representation. A procedure for obtaining the new representation of the data has also been developed, aimed at obtaining a more powerful representation. The proposed approach is evaluated on two threeway data sets. This has been done by comparing the different ways of achieving the new representation, and the traditional vector representation of the objects.