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New clustering methods for interval data

Author

Listed:
  • Marie Chavent
  • Francisco Carvalho
  • Yves Lechevallier
  • Rosanna Verde

Abstract

No abstract is available for this item.

Suggested Citation

  • Marie Chavent & Francisco Carvalho & Yves Lechevallier & Rosanna Verde, 2006. "New clustering methods for interval data," Computational Statistics, Springer, vol. 21(2), pages 211-229, June.
    • Handle: RePEc:spr:compst:v:21:y:2006:i:2:p:211-229
    DOI: 10.1007/s00180-006-0260-0
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    Citations

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    Cited by:

    1. Andrzej Młodak, 2014. "On the construction of an aggregated measure of the development of interval data," Computational Statistics, Springer, vol. 29(5), pages 895-929, October.
    2. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
    3. Áurea Sousa & Osvaldo Silva & Leonor Bacelar-Nicolau & João Cabral & Helena Bacelar-Nicolau, 2023. "Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data," Stats, MDPI, vol. 6(4), pages 1-13, October.
    4. Angela Blanco-Fernández & Peter Winker, 2016. "Data generation processes and statistical management of interval data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(4), pages 475-494, October.
    5. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.

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