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dc.contributor.author | Fritz, Heinrich | |
dc.contributor.author | García Escudero, Luis Ángel | |
dc.contributor.author | Mayo Iscar, Agustín | |
dc.date.accessioned | 2016-12-20T11:46:13Z | |
dc.date.available | 2016-12-20T11:46:13Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Computational Statistics and Data Analysis, 61, 124-136 | es |
dc.identifier.uri | http://uvadoc.uva.es/handle/10324/21849 | |
dc.description.abstract | The 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.mimetype | application/pdf | es |
dc.language.iso | spa | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Estadística | es |
dc.title | A fast algorithm for robust constrained clustering. | es |
dc.type | info:eu-repo/semantics/article | es |
dc.peerreviewed | SI | es |
dc.rights | Attribution 4.0 International |
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