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The dissimilarity representation has demonstrated advantages in the solution of classification problems. Meanwhile, the representation of objects by multi-dimensional arrays is necessary in many research areas. However, the development of proper classification tools that take the multi-way structure into account is incipient. This paper introduces the use of the dissimilarity representation as a tool for classifying three-way data, as dissimilarities allow the representation of multidimensional objects in a natural way. As an example, the classification of three-way seismic volcanic data is used. A comparison is made between dissimilarity measures used in different representations of the three-way data. 2D dissimilarity measures for three-way data can be useful.

The Dissimilarity Representation as a Tool for Three-Way Data Classification: A 2D Measure