RT info:eu-repo/semantics/article T1 Robust Constrained Fuzzy Clustering A1 Fritz, Heinrich A1 García Escudero, Luis Ángel A1 Mayo Iscar, Agustín K1 Estádistica K1 Clustering K1 Fuzzy clustering K1 Noise K1 Outlier K1 Constraint K1 Trimming AB It is well-known that outliers and noisy data can be very harmful when applyingclustering methods. Several fuzzy clustering methods which are ableto handle the presence of noise have been proposed. In this work, we proposea robust clustering approach called F-TCLUST based on an “impartial”(i.e., self-determined by data) trimming. The proposed approach considersan eigenvalue ratio constraint that makes it a mathematically well-definedproblem and serves to control the allowed differences among cluster scatters.A computationally feasible algorithm is proposed for its practical implementation.Some guidelines about how to choose the parameters controlling theperformance of the fuzzy clustering procedure are also given. PB Elsevier SN 0020-0255 YR 2013 FD 2013 LK http://uvadoc.uva.es/handle/10324/21850 UL http://uvadoc.uva.es/handle/10324/21850 LA eng NO Information Sciences, 2013, vol. 245, p. 38-52. NO Producción Científica DS UVaDOC RD 05-abr-2025