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.

García-Escudero, L., Greselin, F., Mayo-Iscar, A. (2017). Fuzzy clustering through robust factor analyzers. In M.B. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, M.A. Gil, P. Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 229-235). Springer Verlag [10.1007/978-3-319-42972-4_29].

Fuzzy clustering through robust factor analyzers

Greselin, F
;
2017

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.
Capitolo o saggio
Fuzzy clustering; membership function; robust estimation; constrained estimation; Maximum likelihood
English
Soft Methods for Data Science
9783319429717
8th International Conference on Soft Methods in Probability and Statistics, SMPS 2016; Rome; Italy; 12 September 2016 through 14 September 2016
García-Escudero, L., Greselin, F., Mayo-Iscar, A. (2017). Fuzzy clustering through robust factor analyzers. In M.B. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, M.A. Gil, P. Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 229-235). Springer Verlag [10.1007/978-3-319-42972-4_29].
García-Escudero, L; Greselin, F; Mayo-Iscar, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/145578
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