3In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated by the empirical issue of detecting low correlations and discriminating variables with very similar rank correlation. This issue arises from the analysis of panel data concerning the district heating demand of the Italian city Bozen-Bolzano. In the hierarchical clustering framework, we empirically investigate the features of the proposed measure and compare it with a classical dissimilarity measure based on Kendall’s rank correlation.openopenF. Marta L. Di Lascio, Andrea Menapace, Roberta PappadàDi Lascio, F. Marta L.; Andrea, Menapace; Pappada', Robert
Conventional clustering algorithms are restricted for use with data containing ratio or interval sca...
There are numerous binary similarity measures. Different binary similarity measures estimate differe...
none2The most frequently used hierarchical methods for clustering of quantitative variables are base...
A new distance measure is defined for ranking data by using copula functions. This distance evaluate...
We define a new distance measure for ranking data using a mixture of copula functions. Our distance ...
We propose a new measure to evaluate the dissimilarity between rankings in hierarchical cluster anal...
We propose a new dissimilarity measure for ranking data by using a mixture of copula functions. This...
A theoretical framework is presented for a (copula-based) notion of dissimilarity between continuous...
We propose a new measure to evaluate the distance between subjects expressing their preferences by r...
We define a new distance measure for ranking data by using a mixture of copula functions. This dista...
We define a new distance measure for ranking data by using a mixture of copula functions. This distan...
<p>A correlation matrix together with clustering (i.e., Pearson uncentered) of the feature points is...
Clustering of ranking data aims at the identification of groups of subjects with a homogenous, com-m...
Abstract: Clustering algorithms have been actively used to identify similar time series, providing a...
Time series clustering with a dissimilarity matrix based on tail dependence coefficients estimated b...
Conventional clustering algorithms are restricted for use with data containing ratio or interval sca...
There are numerous binary similarity measures. Different binary similarity measures estimate differe...
none2The most frequently used hierarchical methods for clustering of quantitative variables are base...
A new distance measure is defined for ranking data by using copula functions. This distance evaluate...
We define a new distance measure for ranking data using a mixture of copula functions. Our distance ...
We propose a new measure to evaluate the dissimilarity between rankings in hierarchical cluster anal...
We propose a new dissimilarity measure for ranking data by using a mixture of copula functions. This...
A theoretical framework is presented for a (copula-based) notion of dissimilarity between continuous...
We propose a new measure to evaluate the distance between subjects expressing their preferences by r...
We define a new distance measure for ranking data by using a mixture of copula functions. This dista...
We define a new distance measure for ranking data by using a mixture of copula functions. This distan...
<p>A correlation matrix together with clustering (i.e., Pearson uncentered) of the feature points is...
Clustering of ranking data aims at the identification of groups of subjects with a homogenous, com-m...
Abstract: Clustering algorithms have been actively used to identify similar time series, providing a...
Time series clustering with a dissimilarity matrix based on tail dependence coefficients estimated b...
Conventional clustering algorithms are restricted for use with data containing ratio or interval sca...
There are numerous binary similarity measures. Different binary similarity measures estimate differe...
none2The most frequently used hierarchical methods for clustering of quantitative variables are base...