activity
20082025
most citedA general trimming approach to robust Cluster Analysis

203 citations · 276 across the 11 of their papers we have counts for

collaborators

11 papers

stat.ME2025

Robust fuzzy clustering with cellwise outliers

Giorgia Zaccaria, Lorenzo Benzakour, Luis A. García-Escudero +2

In a data matrix, we may distinguish between cases, each represented by a row vector for a statistical unit, and cells, which correspond to single entries of the data matrix. Recen…

stat.ME2024★ 3 cited

Cellwise outlier detection in heterogeneous populations

Giorgia Zaccaria, Luis A. García-Escudero, Francesca Greselin +1

Real-world applications may be affected by outlying values. In the model-based clustering literature, several methodologies have been proposed to detect units that deviate from the…

stat.ME2023

Choice of trimming proportion and number of clusters in robust clustering based on trimming

Luis Angel García-Escudero, Christian Hennig, Agustín Mayo-Iscar +2

So-called "classification trimmed likelihood curves" have been proposed as a useful heuristic tool to determine the number of clusters and trimming proportion in trimming-based rob…

stat.ML2019★ 23 cited

optimalFlow: Optimal-transport approach to flow cytometry gating and population matching

Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes +2

Data obtained from Flow Cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements…

stat.ML2017

Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis

Diego Rivera-García, Luis Angel García-Escudero, Agustín Mayo-Iscar +1

In this work a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functi…

stat.CO2017★ 1 cited

Robust clustering for functional data based on trimming and constraints

Diego Rivera-García, Luis Angel García-Escudero, Agustín Mayo-Iscar +1

Many clustering algorithms when the data are curves or functions have been recently proposed. However, the presence of contamination in the sample of curves can influence the perfo…