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A fuzzy approach to robust regression clustering

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Abstract

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.

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Acknowledgments

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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Authors and Affiliations

  1. Dipartimento di Scienze Statistiche, Università di Roma “La Sapienza”, Piazzale Aldo Moro, 5, 00185, Rome, Italy

    Francesco Dotto

  2. Dipartimento di Sanità Pubblica e Malattie Infettive, Università di Roma “La Sapienza”, Piazzale Aldo Moro, 5, 00185, Rome, Italy

    Alessio Farcomeni

  3. Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Paseo de Belén, 7, 47011, Valladolid, Spain

    Luis Angel García-Escudero

  4. Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Ramón y Cajal, 7, 47005, Valladolid, Spain

    Agustín Mayo-Iscar

Authors
  1. Francesco Dotto
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  2. Alessio Farcomeni
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  3. Luis Angel García-Escudero
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  4. Agustín Mayo-Iscar
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Corresponding author

Correspondence to Francesco Dotto.

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Dotto, F., Farcomeni, A., García-Escudero, L.A. et al. A fuzzy approach to robust regression clustering. Adv Data Anal Classif 11, 691–710 (2017). https://doi.org/10.1007/s11634-016-0271-9

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