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.
The dissimilarity representation as a tool for three-way data classification: A 2D measure
Porro Munoz, Diana;
2010-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
