ON DISSIMILARITY MEASUREMENT IN VISUALIZATION OF MULTIDIMENSIONAL DATA

A. Žilinskas and A. Podlipskytė
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A. Žilinskas: Institute of Mathematics and Informatics, VMU, Akademijos str. 4, Vilnius, 08663, Lithuania
A. Podlipskytė: Institute of Psychophysiology and Rehabilitation Vyduno str. 4, Palanga, 00135, Lithuania

Chapter 16 in Computer Aided Methods in Optimal Design and Operations, 2006, pp 149-158 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractMultidimensional scaling (MDS) is a prospective technique to the visualization and exploratory analysis of multidimensional data. By means of MDS algorithms a two dimensional representation of a set of points in a high dimensional (original) space can be obtained, where distances between the points in the two dimensional embedding space represent dissimilarity of multidimensional points. The latter normally is measured by the Euclidean distance, although the alternative measures can be advantageous. In the present paper we investigate influence of the choice of dissimilarity measure (distances in the original space) to the visualization results.

Keywords: Optimization; Optimal Design; Global Optimization; Optimal Control (search for similar items in EconPapers)
Date: 2006
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