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ON DISSIMILARITY MEASUREMENT IN VISUALIZATION OF MULTIDIMENSIONAL DATA

Antanas Žilinskas, Aurelija Podlipskytė

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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.

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What this paper is about

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.

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Available 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.

Key concepts: Multidimensional scaling, Visualization, Multidimensional data, Euclidean space, Set (abstract data type), Representation (politics), Space (punctuation), Measure (data warehouse)

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