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SOMwise regression: a new clusterwise regression method

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Abstract

We present a novel neural learning architecture for regression data analysis. It combines, at the high level, a self-organizing map (SOM) structure, and, at the low level, a multilayer perceptron at each unit of the SOM structure. The goal is to build a clusterwise regression model, that is, a model recognizing several clusters in the data, where the dependence between predictors and response is variable (typically within some parametric range) from cluster to cluster. The proposed algorithm, called SOMwise Regression, follows closely in the spirit of the standard SOM learning algorithm and has performed satisfactorily on various test problems.

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Acknowledgments

This research was partially supported by projects TIN2007-62626 and Cajal Blue Brain. We are very grateful to Prof. Concha Bielza and Prof. Pedro Larrañaga for heir valuable support. Finally, we would like to express our very special gratitude in the memory of the first author, Prof. Jorge Muruzábal, who devised the idea and provided the inspiration for this and many others papers. It was a pleasure to work with him and share his enthusiastic attitude to the science.

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Authors and Affiliations

  1. Universidad Rey Juan Carlos, Madrid, Spain

    Jorge Muruzábal & Julián Sánchez

  2. Computational Intelligence Group, Universidad Politécnica de Madrid, Madrid, Spain

    Diego Vidaurre

Authors
  1. Jorge Muruzábal
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  2. Diego Vidaurre
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  3. Julián Sánchez
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Corresponding author

Correspondence to Diego Vidaurre.

Additional information

J. Muruzábal: Deceased

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Muruzábal, J., Vidaurre, D. & Sánchez, J. SOMwise regression: a new clusterwise regression method. Neural Comput & Applic 21, 1229–1241 (2012). https://doi.org/10.1007/s00521-011-0536-3

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  • DOI: https://doi.org/10.1007/s00521-011-0536-3

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