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Título: Robust Fuzzy Clustering via Trimming and Constraints
Autor: Dotto, Francesco
Farcomeni, Alessio
García Escudero, Luis Ángel
Mayo Iscar, Agustín
Año del Documento: 2016
Editorial: Springer International Publishing
Descripción: Producción Científica
Documento Fuente: Soft Methods for Data Science. Editors: Maria Brigida Ferraro, Paolo Giordani , Barbara Vantaggi , Marek Gagolewski , María Ángeles Gil, Przemysław Grzegorzewski , Olgierd Hryniewicz , Springer International Publishing, 2016, p. 197-204 (Advances in Intelligent Systems and Computing, 456)
Resumen: A methodology for robust fuzzy clustering is proposed. This methodology can be widely applied in very different statistical problems given that it is based on probability likelihoods. Robustness is achieved by trimming a fixed proportion of “most outlying” observations which are indeed self-determined by the data set at hand. Constraints on the clusters’ scatters are also needed to get mathematically well-defined problems and to avoid the detection of non-interesting spurious clusters. The main lines for computationally feasible algorithms are provided and some simple guidelines about how to choose tuning parameters are briefly outlined. The proposed methodology is illustrated through two applications. The first one is aimed at heterogeneously clustering under multivariate normal assumptions and the second one migh be useful in fuzzy clusterwise linear regression problems.
Materias (normalizadas): Statistics
ISBN: 978-3-319-42972-4
DOI: 10.1007/978-3-319-42972-4_25
Patrocinador: Ministerio de Economía, Industria y Competitividad (MTM2014-56235-C2-1-P)
Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA212U13)
Version del Editor:
Idioma: eng
Derechos: info:eu-repo/semantics/openAccess
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