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Dynamic clustering for interval data based on L 2 distance

Author

Listed:
  • Francisco Carvalho
  • Paula Brito
  • Hans-Hermann Bock

Abstract

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Suggested Citation

  • Francisco Carvalho & Paula Brito & Hans-Hermann Bock, 2006. "Dynamic clustering for interval data based on L 2 distance," Computational Statistics, Springer, vol. 21(2), pages 231-250, June.
    • Handle: RePEc:spr:compst:v:21:y:2006:i:2:p:231-250
    DOI: 10.1007/s00180-006-0261-z
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    Citations

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    Cited by:

    1. A. Pedro Duarte Silva & Peter Filzmoser & Paula Brito, 2018. "Outlier detection in interval data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 785-822, September.
    2. M. Rosário Oliveira & Margarida Azeitona & António Pacheco & Rui Valadas, 2022. "Association measures for interval variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 491-520, September.
    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. Guo, Junpeng & Li, Wenhua & Li, Chenhua & Gao, Sa, 2012. "Standardization of interval symbolic data based on the empirical descriptive statistics," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 602-610.
    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.
    6. Nataša Kejžar & Simona Korenjak-Černe & Vladimir Batagelj, 2021. "Clustering of modal-valued symbolic data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 513-541, June.
    7. Andrzej Młodak, 2011. "Classification of Multivariate Objects Using Interval Quantile Classes," Journal of Classification, Springer;The Classification Society, vol. 28(3), pages 327-362, October.
    8. Ana Belén Ramos-Guajardo, 2022. "A hierarchical clustering method for random intervals based on a similarity measure," Computational Statistics, Springer, vol. 37(1), pages 229-261, March.
    9. 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.
    10. Fei Liu & L. Billard, 2022. "Partition of Interval-Valued Observations Using Regression," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 55-77, March.

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