2026-04-27T08:15:57Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/387942025-03-26T19:10:03Zcom_10324_1151com_10324_931com_10324_894col_10324_1278
UVaDOC
author
García Escudero, Luis Ángel
author
Greselin, Francesca
author
Mayo Iscar, Agustín
2019-10-28T17:21:46Z
2019-10-28T17:21:46Z
2018
International Journal of Approximate Reasoning (2018), Vol. 94, 60–75
http://uvadoc.uva.es/handle/10324/38794
10.1016/j.ijar.2018.01.001
A clustering algorithm that combines the advantages of fuzzy clustering and robust statistical estimators is presented. It is based on mixtures of Factor Analyzers, endowed by the joint usage of trimming and the constrained estimation of scatter matrices, in a modified maximum likelihood approach. The algorithm generates a set of membership values, that are used to fuzzy partition the data set and to contribute to the robust estimates of the mixture parameters. The adoption of clusters modeled by Gaussian Factor Analysis allows for dimension reduction and for discovering local linear structures in the data. The new methodology has been shown to be resistant to different types of contamination, by applying it on artificial data. A brief discussion on the tuning parameters, such as the trimming level, the fuzzifier parameter, the number of clusters and the value of the scatter matrices constraint, has been developed, also with the help of some heuristic tools for their choice. Finally, a real data set has been analyzed, to show how intermediate membership values are estimated for observations lying at cluster overlap, while cluster cores are composed by observations that are assigned to a cluster in a crisp way.
spa
Robust, fuzzy, and parsimonious clustering based on mixtures of Factor Analyzers
info:eu-repo/semantics/article
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URL
https://uvadoc.uva.es/bitstream/10324/38794/1/rob_fuzzy_MFA_repos.pdf
File
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application/pdf
rob_fuzzy_MFA_repos.pdf