A fuzzy approach to robust regression clustering
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A new robust fuzzy regression clustering method is proposed. We estimate coefficients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a fixed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed method seems to be robust with respect to different types of contamination.
KeywordsRobustness Fuzzy clustering Trimming Regression clustering
Mathematics Subject Classification62H30
The authors are grateful to three referees and the Associated Editor for several constructive suggestions. Research partially supported by the Spanish Ministerio de Economía y Competitividad, Grant MTM2014-56235-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León, Grant VA212U13.
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