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.
Año del Documento
2013
Documento Fuente
Computational Statistics and Data Analysis, 61, 124-136
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.
Materias (normalizadas)
Estadística
Revisión por pares
SI
Idioma
spa
Derechos
openAccess
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Except where otherwise noted, this item's license is described as Attribution 4.0 International