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On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

This paper gives a clusterwise linear regression method aiming to provide linear regression models that are based on homogeneous clusters of observations w.r.t. the explanatory variables. To achieve this aim, this method combine the standard clusterwise linear regression and K-Means with automatic computation of relevance weights for the explanatory variables. Experiments with benchmark datasets corroborate the usefulness of the proposed method.

The authors would like to thanks CNPq and FACEPE (Brazilian agencies) for their financial support.

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References

  1. Alpaydin, E.: Introduction to machine learning. In: Adaptive Computation and Machine Learning. MIT Press (2014)

    Google Scholar 

  2. Brusco, M.J., Cradit, J.D., Steinley, D., Fox, G.L.: Cautionary remarks on the use of clusterwise regression. Multivar. Behav. Res. 43(1), 29–49 (2008)

    Article  Google Scholar 

  3. Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Trans. Neural Netw. 27, 801–804 (2005)

    Google Scholar 

  4. Diday, E., Govaert, G.: Classification automatique avec distances adaptatives. R.A.I.R.O. Informatique Comput. Sci. 11(4), 329–349 (1977)

    MATH  MathSciNet  Google Scholar 

  5. Hathaway, R.J., Bezdek, J.C.: Switching regression models and fuzzy clustering. IEEE Trans. Fuzzy Syst. 1(3), 195–204 (1993)

    Article  Google Scholar 

  6. Huang, J., Ng, M., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 657–668 (2005)

    Article  Google Scholar 

  7. Krstajic, D., Buturovic, L.J., Leahy, D.E., Thomas, S.: Cross-validation pitfalls when selecting and assessing regression and classification models. J. Chem-inform. 6(1), 10 (2014)

    Google Scholar 

  8. Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  9. Manwani, N., Sastry, P.: K-plane regression. Inf. Sci. 292, 39–56 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  10. Modha, D.S., Spangler, W.S.: Feature weighting in k-means clustering. Mach. Learn. 52(3), 217–237 (2003)

    Article  MATH  Google Scholar 

  11. Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to linear regression analysis (2001)

    Google Scholar 

  12. Ryoke, M., Nakamori, Y., Suzuki, K.: Adaptive fuzzy clustering and fuzzy prediction models. In: Proceedings of 1995 IEEE International Fuzzy Systems, pp. 2215–2220. IEEE (1995)

    Google Scholar 

  13. Späth, H.: Algorithm 39 clusterwise linear regression. Computing 22(4), 367–373 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  14. Vicari, D., Vichi, M.: Multivariate linear regression for heterogeneous data. J. Appl. Stat. 40(6), 1209–1230 (2013)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Centro de Informatica - CIn, Universidade Federal de Pernambuco, Recife, Brazil

    Ricardo A. M. da Silva & Francisco de A. T. de Carvalho

Authors
  1. Ricardo A. M. da Silva
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  2. Francisco de A. T. de Carvalho
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Corresponding author

Correspondence to Francisco de A. T. de Carvalho .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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da Silva, R.A.M., de Carvalho, F.d.A.T. (2017). On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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