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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/21849
Título: A fast algorithm for robust constrained clustering.
Autor: Fritz, Heinrich
García Escudero, Luis Angel
Mayo Iscar, Agustín
Año del Documento: 2013
Documento Fuente: Computational Statistics and Data Analysis, 61, 124-136
Resumen: 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.
Materias (normalizadas): Estadística
Revisión por Pares: SI
Idioma: spa
URI: http://uvadoc.uva.es/handle/10324/21849
Derechos: info:eu-repo/semantics/openAccess
Aparece en las colecciones:DEP24 - Artículos de revista

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