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Copula–based clustering methods

In: Copulas and Dependence Models with Applications

Author

Listed:
  • 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”)

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.

Suggested Citation

  • F. Marta L. Di Lascio & Fabrizio Durante & Roberta Pappadà, 2017. "Copula–based clustering methods," Springer Books, in: Manuel Úbeda Flores & Enrique de Amo Artero & Fabrizio Durante & Juan Fernández Sánchez (ed.), Copulas and Dependence Models with Applications, chapter 0, pages 49-67, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64221-5_4
    DOI: 10.1007/978-3-319-64221-5_4
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