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Title: Fuzzy Clustering Throug Robust Factor Analyzers
Authors: García Escudero, Luis Ángel
Greselin, Francesca
Mayo Iscar, Agustín
Issue Date: 2016
Publisher: Springer International Publishing
Description: Producción Científica
Citation: 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.
Keywords: Statistics
ISBN: 978-3-319-42971-7
DOI: 10.1007/978-3-319-42972-4_29
Publisher Version:
Language: eng
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:DEP24 - Capítulos de monografías

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