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.
Citazione: | 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. |
Titolo: | Fuzzy clustering through robust factor analyzers |
Autori: | García-Escudero, L; Greselin, F; Mayo-Iscar, A |
Autori: | |
Presenza di un coautore afferente ad Istituzioni straniere: | Si |
Tipo: | Capitolo o saggio |
Carattere della pubblicazione: | Scientifica |
Data di pubblicazione: | 2017 |
Lingua: | English |
Titolo del libro: | Soft Methods for Data Science |
ISBN: | 9783319429717 |
Appare nelle tipologie: | 03 - Contributo in libro |
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