2024-03-28T19:52:16Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/218502021-06-23T10:09:54Zcom_10324_1151com_10324_931com_10324_894col_10324_1278
Robust Constrained Fuzzy Clustering
Fritz, Heinrich
García Escudero, Luis Ángel
Mayo Iscar, Agustín
Estádistica
It is well-known that outliers and noisy data can be very harmful when applying
clustering methods. Several fuzzy clustering methods which are able
to handle the presence of noise have been proposed. In this work, we propose
a robust clustering approach called F-TCLUST based on an “impartial”
(i.e., self-determined by data) trimming. The proposed approach considers
an eigenvalue ratio constraint that makes it a mathematically well-defined
problem 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 the
performance of the fuzzy clustering procedure are also given.
2016-12-20T11:48:32Z
2016-12-20T11:48:32Z
2013
info:eu-repo/semantics/article
Information Sciences, 245, 38-52.
http://uvadoc.uva.es/handle/10324/21850
spa
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 International
SI