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On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling

Published online by Cambridge University Press:  01 January 2025

J. Fernando Vera Open the ORCID record for J. Fernando Vera [Opens in a new window]  and
Rodrigo Macías Open the ORCID record for Rodrigo Macías [Opens in a new window]
J. Fernando Vera*
Affiliation:
University of Granada
Rodrigo Macías
Affiliation:
Centro De Investigación En Matemáticas
*
Correspondence should be made to J. Fernando Vera, Unidad Monterey, University of Granada, Granada, Spain. Email: jfvera@ugr.es
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Abstract

In this article, we analyse the usefulness of multidimensional scaling in relation to performing K-means clustering on a dissimilarity matrix, when the dimensionality of the objects is unknown. In this situation, traditional algorithms cannot be used, and so K-means clustering procedures are being performed directly on the basis of the observed dissimilarity matrix. Furthermore, the application of criteria originally formulated for two-mode data sets to determine the number of clusters depends on their possible reformulation in a one-mode situation. The linear invariance property in K-means clustering for squared dissimilarities, together with the use of multidimensional scaling, is investigated to determine the cluster membership of the observations and to address the problem of selecting the number of clusters in K-means for a dissimilarity matrix. In particular, we analyse the performance of K-means clustering on the full dimensional scaling configuration and on the equivalently partitioned configuration related to a suitable translation of the squared dissimilarities. A Monte Carlo experiment is conducted in which the methodology examined is compared with the results obtained by procedures directly applicable to a dissimilarity matrix.

Keywords

cluster analysisK-meansmultidimensional scalingadditive constantdissimilarityclustering criteria
Type
Theory and Methods
Information
Psychometrika , Volume 86 , Issue 2 , June 2021 , pp. 489 - 513
Copyright
Copyright © 2021 The Psychometric Society

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