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A review of robust clustering methods

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  • Luis García-Escudero

    ()

  • Alfonso Gordaliza

    ()

  • Carlos Matrán

    ()

  • Agustín Mayo-Iscar

    ()

Abstract

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Suggested Citation

  • Luis García-Escudero & Alfonso Gordaliza & Carlos Matrán & Agustín Mayo-Iscar, 2010. "A review of robust clustering methods," 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. 4(2), pages 89-109, September.
  • Handle: RePEc:spr:advdac:v:4:y:2010:i:2:p:89-109
    DOI: 10.1007/s11634-010-0064-5
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    References listed on IDEAS

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    1. Van Aelst, Stefan & (Steven) Wang, Xiaogang & Zamar, Ruben H. & Zhu, Rong, 2006. "Linear grouping using orthogonal regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1287-1312, March.
    2. Domenico Perrotta & Marco Riani & Francesca Torti, 2009. "New robust dynamic plots for regression mixture detection," 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. 3(3), pages 263-279, December.
    3. J. A. Cuesta-Albertos & C. Matrán & A. Mayo-Iscar, 2008. "Robust estimation in the normal mixture model based on robust clustering," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 779-802.
    4. Schyns, M. & Haesbroeck, G. & Critchley, F., 2010. "RelaxMCD: Smooth optimisation for the Minimum Covariance Determinant estimator," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 843-857, April.
    5. María Gallegos & Gunter Ritter, 2009. "Trimming algorithms for clustering contaminated grouped data and their robustness," 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. 3(2), pages 135-167, September.
    6. Bock, Hans H., 1996. "Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 5-28, November.
    7. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    8. Luis Angel Garcia-Escudero & Alfonso Gordaliza, 2005. "A Proposal for Robust Curve Clustering," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 185-201, September.
    9. Atkinson, A.C. & Riani, M., 2007. "Exploratory tools for clustering multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 272-285, September.
    10. L. A. García-Escudero & A. Gordaliza & R. San Martín & S. Van Aelst & R. Zamar, 2009. "Robust linear clustering," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 301-318.
    11. Garcia-Escudero, Luis Angel & Gordaliza, Alfonso, 2005. "Generalized Radius Processes for Elliptically Contoured Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1036-1045, September.
    12. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2001. "Cluster analysis: a further approach based on density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 441-459, June.
    13. Croux, Christophe & Gallopoulos, Efstratios & Van Aelst, Stefan & Zha, Hongyuan, 2007. "Machine Learning and Robust Data Mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 151-154, September.
    14. Cuesta-Albertos, J. A. & García-Escudero, L. A. & Gordaliza, A., 2002. "On the Asymptotics of Trimmed Best k-Nets," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 486-516, August.
    15. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
    16. Cuesta-Albertos, Juan Antonio & Fraiman, Ricardo, 2007. "Impartial trimmed k-means for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4864-4877, June.
    17. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
    18. Garcia-Escudero, L.A. & Gordaliza, A., 2007. "The importance of the scales in heterogeneous robust clustering," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4403-4412, May.
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    Citations

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    Cited by:

    1. Cardot, Hervé & Cénac, Peggy & Monnez, Jean-Marie, 2012. "A fast and recursive algorithm for clustering large datasets with k-medians," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1434-1449.
    2. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
    3. repec:eee:csdana:v:113:y:2017:i:c:p:475-496 is not listed on IDEAS
    4. Slaets, Leen & Claeskens, Gerda & Hubert, Mia, 2012. "Phase and amplitude-based clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2360-2374.
    5. Fritz, Heinrich & García-Escudero, Luis A. & Mayo-Iscar, Agustín, 2013. "A fast algorithm for robust constrained clustering," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 124-136.

    More about this item

    Keywords

    Clustering; Robustness; Model-based clustering; Trimming; 62h30; 62G35;

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