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.

Copula–based clustering methods

PAPPADA', ROBERTA
2017-01-01

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.
2017
978-3-319-64220-8
978-3-319-64221-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2906626
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