Copula–based clustering methods

F. Marta L. Di Lascio (), Fabrizio Durante () and Roberta Pappadà ()
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F. Marta L. Di Lascio: Free University of Bozen-Bolzano, Faculty of Economics and Management
Fabrizio Durante: Università del Salento, Dipartimento di Scienze dell’Economia
Roberta Pappadà: University of Trieste, Department of Economics, Business, Mathematics and Statistics “Bruno de Finetti”

Chapter Chapter 4 in Copulas and Dependence Models with Applications, 2017, pp 49-67 from Springer

Abstract: Abstract We review some recent clustering methods based on copulas. Specifically, in the dissimilarity–based clustering framework, we describe and compare methods based on concordance or tail-dependence concept. An illustration is hence provided by using a time series dataset formed by the constituent data of the S&P 500 observed during the financial crisis of 2007-2008. Next, in the likelihood–based clustering framework, we present and discuss a clustering algorithm based on copula and called CoClust. Here, an application to the gene expression profiles of human tumour cell lines is provided to describe the methodology. Finally, a comparison between the two different approaches is performed through a case study on environmental data.

Date: 2017
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DOI: 10.1007/978-3-319-64221-5_4

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