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dc.contributor.authorFritz, Heinrich
dc.contributor.authorGarcía Escudero, Luis Ángel 
dc.contributor.authorMayo Iscar, Agustín 
dc.date.accessioned2016-12-20T11:46:13Z
dc.date.available2016-12-20T11:46:13Z
dc.date.issued2013
dc.identifier.citationComputational Statistics and Data Analysis, 61, 124-136es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/21849
dc.description.abstractThe application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and Rousseeuw and van Driessen’s fast-MCD algorithm. Despite this coincidence, it is not completely straightforward to combine both algorithms for developing a clustering method which is not severely affected by few outlying observations and being able to cope with non spherical clusters. A sensible way of combining them relies on controlling the relative cluster scatters through constrained concentration steps. With this idea in mind, a new algorithm for the TCLUST robust clustering procedure is proposed which implements such constrained concentration steps in a computationally efficient fashion.es
dc.format.mimetypeapplication/pdfes
dc.language.isospaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEstadísticaes
dc.titleA fast algorithm for robust constrained clustering.es
dc.typeinfo:eu-repo/semantics/articlees
dc.peerreviewedSIes
dc.rightsAttribution 4.0 International


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