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In this work, we present a novel method for hierarchically variable
clustering using singular value decomposition. Our proposed approach provides a
non-parametric solution to identify block diagonal patterns in covariance
(correlation) matrices, thereby grouping variables according to their
dissimilarity. We explain the methodology and outline the incorporation of
linkage functions to assess dissimilarities between clusters. To validate the
efficiency of our method, we perform both a simulation study and an analysis of
real-world data. Our findings show the approach's robustness. We conclude by
discussing potential extensions and future directions for research in this
field. Supplementary materials for this article can be accessed online.
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