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Título
A fast algorithm for robust constrained clustering.
Año del Documento
2013
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
Computational Statistics and Data Analysis, 2013, vol. 61, p. 124-136
Zusammenfassung
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
Palabras Clave
Cluster analysis
Robustness
Impartial trimming
Classification EM algorithm
TCLUST
ISSN
0167-9473
Revisión por pares
SI
Patrocinador
Ministerio de Ciencia e Innovación (MTM2011-28657-C02-01)
Version del Editor
Propietario de los Derechos
© 2012 Elsevier
Idioma
eng
Tipo de versión
info:eu-repo/semantics/submittedVersion
Derechos
openAccess
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