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dc.contributor.authorDotto, Francesco
dc.contributor.authorFarcomeni, Alessio
dc.contributor.authorGarcía Escudero, Luis Ángel 
dc.contributor.authorMayo Iscar, Agustín 
dc.date.accessioned2018-10-05T21:53:50Z
dc.date.available2018-10-05T21:53:50Z
dc.date.issued2018
dc.identifier.citationStatistics and Computing, Vol. 28, 477–493es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/32022
dc.description.abstractAn 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.es
dc.format.mimetypeapplication/pdfes
dc.language.isospaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.titleA Reweighting Approach to Robust Clusteringes
dc.typeinfo:eu-repo/semantics/articlees
dc.peerreviewedSIes
dc.description.projectSpanish 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.es


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