RT info:eu-repo/semantics/article
T1 Robust, fuzzy, and parsimonious clustering based on mixtures of Factor Analyzers
A1 García Escudero, Luis Ángel
A1 Greselin, Francesca
A1 Mayo Iscar, Agustín
AB 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.
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/38794
UL http://uvadoc.uva.es/handle/10324/38794
LA spa
NO International Journal of Approximate Reasoning (2018), Vol. 94, 60–75
DS UVaDOC
RD 11-jul-2024