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Título
A Reweighting Approach to Robust Clustering
Año del Documento
2018
Editorial
Springer
Descripción
Producción Científica
Documento Fuente
Statistics and Computing, 2018, vol. 28, p.477-493
Zusammenfassung
An iteratively reweighted approach for robust clustering is presented in
this work. The method is initialized with a very robust clustering partition
based on an high trimming level. The initial partition is then refined
to reduce the number of wrongly discarded observations and substantially
increase efficiency. Simulation studies and real data examples indicate that
the final clustering solution is both robust and efficient, and naturally adapts
to the true underlying contamination level.
Palabras Clave
Cluster analysis
Trimming
Robustness
Minimum covariance determinant estimator
ISSN
0960-3174
Revisión por pares
SI
Patrocinador
Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18.
Version del Editor
Propietario de los Derechos
© Springer Science+Business Media New York 2017
Idioma
eng
Tipo de versión
info:eu-repo/semantics/submittedVersion
Derechos
openAccess
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