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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/21840
Título: Fuzzy Clustering Throug Robust Factor Analyzers
Autor: García Escudero, Luis Ángel
Greselin, Francesca
Mayo Iscar, Agustín
Año del Documento: 2016
Editorial: Springer International Publishing
Descripción: Producción Científica
Documento Fuente: Soft Methods for Data Science. Editors: Ferraro, M.B., Giordani, P., Vantaggi, B., Gagolewski, M., Ángeles Gil, M., Grzegorzewski, P., Hryniewicz, O. Springer International Publishing, 2016, p. 229-235 ( Advances in Intelligent Systems and Computing, 456)
Resumen: In fuzzy clustering, data elements can belong to more than one cluster , and membership levels are associated with each element, to indicate the strength of the association between that data element and a particular cluster. Unfortunately, fuzzy clustering is not robust, while in real applications the data is contaminated by outliers and noise, and the assumed underlying Gaussian distributions could be unrealistic. Here we propose a robust fuzzy estimator for clustering through Factor Analyzers, by introducing the joint usage of trimming and of constrained estimation of noise matrices in the classic Maximum Likelihood approach.
Materias (normalizadas): Statistics
ISBN: 978-3-319-42971-7
DOI: 10.1007/978-3-319-42972-4_29
Version del Editor: http://link.springer.com/chapter/10.1007/978-3-319-42972-4_29
Idioma: eng
URI: http://uvadoc.uva.es/handle/10324/21840
Derechos: info:eu-repo/semantics/openAccess
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