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Classification as a Tool for Research pp 167–175Cite as

Clustering Spatio-Functional Data: A Model Based Approach

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Abstract

In many environmental sciences, such as, in agronomy, in metereology, in oceanography, data analysis has to take into account both spatial and functional components. In this paper we present a strategy for clustering spatio-functional data. The proposed methodology is based on concepts of spatial statistics theory, such as variogram and covariogram when data are curves. Moreover a summarizing spatio-functional model for each cluster is obtained. The assessment of the method is carried out with a study on real data.

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References

  • Abraham, C., Corillon, P., Matnzer-Löber, E., & Molinari, N. (2005). Unsupervised curve clustering using B-splines. Scandinavian Journal of Statistics, 30, 581–595.

    Google Scholar 

  • Blekas, K., Nikou, C., Galatsanos, N., & Tsekos, N. V. (2007). Curve clustering with spatial constraints for analysis of spatiotemporal data. In Proceedings of the 19th IEEE international Conference on Tools with Artificial intelligence (Vol. 1, pp. 529–535). October 29–31. ICTAI. IEEE Computer Society, Washington, DC.

    Google Scholar 

  • Delicado, P., Giraldo, R., & Mateu, J. (2007). Geostatistics for functional data: An ordinary kriging approach. Technical Report, http://hdl.handle.net/2117/1099, Universitat Politecnica de Catalunya.

  • Delicado, P., Giraldo, R., Comas, C., & Mateu, J. (2009). Statistics for spatial functional data: some recent contributions. Environmetrics, 21(3–4), 224–239.

    Article  Google Scholar 

  • Diday, E. (1971). La Mthode des nues dynamiques. Review on Statistical Application, XXX(2), 19–34.

    Google Scholar 

  • Heckman, N., & Zamar, R. (2000). Comparing the shapes of regression functions. Biometrika, 87, 135–144.

    Article  MATH  MathSciNet  Google Scholar 

  • James, G., & Sugar, C. (2005). Clustering for sparsely sampled functional data. Journal of the American Statistical Association, 98, 397–408.

    Article  MathSciNet  Google Scholar 

  • Ramsay, J. O. (2008). Fda problems that I like to talk about. Personal communication.

    Google Scholar 

  • Ramsay, J. E., & Silverman, B. W. (2005). Functional data analysis (2nd ed.) Berlin, Heidelberg, New York: Springer.

    Google Scholar 

  • Romano, E. (2006). Dynamical curves clustering with free knots spline estimation. PHD Thesis. University of Federico II, Naples.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Facoltá di Studi Politici, Dipartimento di Studi Europei e Mediterranei, Seconda Universitá degli Studi di Napoli, Via del Setificio 15, 81100, Caserta, Italy

    Elvira Romano

Authors
  1. Elvira Romano
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  2. Antonio Balzanella
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  3. Rosanna Verde
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Corresponding author

Correspondence to Elvira Romano .

Editor information

Editors and Affiliations

  1. LS für BWL, insb. Finanzwirtschaft und, Finanzdienstleistungen, TU Dresden, Helmholtzstr. 10, Dresden, 01062, Germany

    Hermann Locarek-Junge

  2. FG Computergestützte Statistik, Univ. Dortmund, Vogelpothsweg 87, Dortmund, 44227, Germany

    Claus Weihs

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Romano, E., Balzanella, A., Verde, R. (2010). Clustering Spatio-Functional Data: A Model Based Approach. In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_17

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