Multidimensional scaling of fuzzy dissimilarity data
Multidimensional scaling is a well-known technique for representing measurements of dissimilarity among objects as distances between points in a p-dimensional space. In this paper, this method is extended to the case where dissimilarities are expressed as intervals or fuzzy numbers. Each object is then no longer represented by a point but by a crisp or a fuzzy region. To determine these regions, two algorithms are proposed and illustrated using typical datasets. Experiments demonstrate the ability of the methods to represent both the structure and the vagueness of dissimilarity measurements. (C) 2002 Elsevier Science B.V. All rights reserved.
multidimensional scaling fuzzy dissimilarity fuzzy data analysis