Publication
Title
Pooled variable scaling for cluster analysis
Author
Abstract
Motivation Many popular clustering methods are not scale-invariant because they are based on Euclidean distances. Even methods using scale-invariant distances, such as the Mahalanobis distance, lose their scale invariance when combined with regularization and/or variable selection. Therefore, the results from these methods are very sensitive to the measurement units of the clustering variables. A simple way to achieve scale invariance is to scale the variables before clustering. However, scaling variables is a very delicate issue in cluster analysis: A bad choice of scaling can adversely affect the clustering results. On the other hand, reporting clustering results that depend on measurement units is not satisfactory. Hence, a safe and efficient scaling procedure is needed for applications in bioinformatics and medical sciences research. Results We propose a new approach for scaling prior to cluster analysis based on the concept of pooled variance. Unlike available scaling procedures, such as the SD and the range, our proposed scale avoids dampening the beneficial effect of informative clustering variables. We confirm through an extensive simulation study and applications to well-known real-data examples that the proposed scaling method is safe and generally useful. Finally, we use our approach to cluster a high-dimensional genomic dataset consisting of gene expression data for several specimens of breast cancer cells tissue obtained from human patients.
Language
English
Source (journal)
Bioinformatics. - Oxford, 1998, currens
Publication
Oxford : Oxford University Press , 2020
ISSN
1367-4803 [print]
1367-4811 [online]
DOI
10.1093/BIOINFORMATICS/BTAA243
Volume/pages
36 :12 (2020) , p. 3849-3855
ISI
000550127500030
Pubmed ID
32282889
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 27.02.2024
Last edited 07.03.2024
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