Abstract
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 multi-dimensional 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. © 2010 Springer-Verlag Berlin Heidelberg.
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Porro-Muñoz, D., Duin, R. P. W., Orozco-Alzate, M., Talavera, I., & Londoño-Bonilla, J. M. (2010). The dissimilarity representation as a tool for three-way data classification: A 2D measure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6218 LNCS, pp. 569–578). https://doi.org/10.1007/978-3-642-14980-1_56
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