57 citations · 138 across the 8 of their papers we have counts for
8 papers
On the use of Wasserstein metric in topological clustering of distributional data
Guénaël Cabanes, Younès Bennani, Rosanna Verde +1
This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the da…
Batch Self Organizing maps for distributional data using adaptive distances
Antonio Irpino, Francisco De Carvalho, Rosanna Verde +1
The paper deals with a Batch Self Organizing Map algorithm (DBSOM) for data described by distributional-valued variables. This kind of variables is characterized to take as values…
Multiple factor analysis of distributional data
Rosanna Verde, Antonio Irpino
In the framework of Symbolic Data Analysis (SDA), distribution-variables are a particular case of multi-valued variables: each unit is represented by a set of distributions (e.g. h…
Fuzzy clustering of distribution-valued data using adaptive L2 Wasserstein distances
Antonio Irpino, Francisco De Carvalho, Rosanna Verde
Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy…
Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance
Antonio Irpino, Rosanna Verde
In this paper we present a linear regression model for modal symbolic data. The observed variables are histogram variables according to the definition given in the framework of Sym…
Dynamic Clustering of Histogram Data Based on Adaptive Squared Wasserstein Distances
Antonio Irpino, Rosanna Verde, Francisco de AT De Carvalho
This paper deals with clustering methods based on adaptive distances for histogram data using a dynamic clustering algorithm. Histogram data describes individuals in terms of empir…
Basic statistics for probabilistic symbolic variables: a novel metric-based approach
Antonio Irpino, Rosanna Verde
In data mining, it is usually to describe a set of individuals using some summaries (means, standard deviations, histograms, confidence intervals) that generalize individual descri…
Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering
Elvira Romano, Antonio Balzanella, Rosanna Verde
In several environmental applications data are functions of time, essentially con- tinuous, observed and recorded discretely, and spatially correlated. Most of the methods for anal…