Dissimilarity functions for rank-based hierarchical clustering of continuous variables
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We present a theoretical framework for a (copula-based) notion of dissimilarity between subsets of continuous random variables and study its main properties. Special attention is paid to those properties that are prone to the hierarchical agglomerative methods, such as reducibility. We hence provide insights for the use of such a measure in clustering algorithms, which allows us to cluster random variables according to the association/dependence among them, and present a simulation study. Real case studies illustrate the whole methodology.
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