RT info:eu-repo/semantics/article
T1 A Reweighting Approach to Robust Clustering
A1 Dotto, Francesco
A1 Farcomeni, Alessio
A1 García Escudero, Luis Ángel
A1 Mayo Iscar, Agustín
K1 Cluster analysis
K1 Trimming
K1 Robustness
K1 Minimum covariance determinant estimator
AB An iteratively reweighted approach for robust clustering is presented inthis work. The method is initialized with a very robust clustering partitionbased on an high trimming level. The initial partition is then refinedto reduce the number of wrongly discarded observations and substantiallyincrease efficiency. Simulation studies and real data examples indicate thatthe final clustering solution is both robust and efficient, and naturally adaptsto the true underlying contamination level.
PB Springer
SN 0960-3174
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/32022
UL http://uvadoc.uva.es/handle/10324/32022
LA eng
NO Statistics and Computing, 2018, vol. 28, p.477-493
NO Producción Científica
DS UVaDOC
RD 03-abr-2025