Time series clustering with a dissimilarity matrix based on tail dependence coefficients
estimated by copula functions has been proposed in 2011 by De Luca and Zuccolotto, who …

This paper contributes to the existing literature on the analysis of spatial time series
presenting a new clustering algorithm called COFUST, ie COpula-based FUzzy clustering …

This paper provides a short overview of space–time series clustering, which can be
generally grouped into three main categories such as: hierarchical, partitioning-based, and …

This article considers rank-invariant clustering of continuous data via copula-based Φ-
dependence measures. The general theoretical framework establishes dependence …

In this work, we use a copula-based approach to select the most important features for a
random forest classification. Based on associated copulas between these features, we carry …

We propose a direct and natural extension of Azadkia & Chatterjee's rank correlation $ T $
introduced in [4] to a set of $ q\geq 1$ endogenous variables. The approach builds upon …

Investigating thermal energy demand is crucial for developing sustainable cities and the
efficient use of renewable sources. Despite the advances made in this field, the analysis of …

Correlation-based hierarchical clustering methods for time series typically are based on a
suitable dissimilarity matrix derived from pairwise measures of association. Here, this …

A theoretical framework is presented for a (copula-based) notion of dissimilarity between
continuous random vectors and its main properties are studied. The proposed dissimilarity …

A rank-invariant clustering of variables is introduced that is based on the predictive strength
between groups of variables, ie, two groups are assigned a high similarity if the variables in …