RT info:eu-repo/semantics/bookPart
T1 Fuzzy Clustering Throug Robust Factor Analyzers
A1 García Escudero, Luis Ángel
A1 Greselin, Francesca
A1 Mayo Iscar, Agustín
K1 Statistics
AB 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.
PB Springer International Publishing
SN 978-3-319-42971-7
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21840
UL http://uvadoc.uva.es/handle/10324/21840
LA eng
NO 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)
NO Producción Científica
DS UVaDOC
RD 28-abr-2024