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Título
Fuzzy Clustering Throug Robust Factor Analyzers
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)
Abstract
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
Version del Editor
Idioma
eng
Derechos
openAccess
Collections
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