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Simultaneous curve registration and clustering for functional data

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  • Liu, Xueli
  • Yang, Mark C.K.
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    Abstract

    Study of dynamic processes in many areas of science has led to the appearance of functional data sets. It is often the case that individual trajectories vary both in the amplitude space and in the time space. We develop a coherent clustering procedure that allows for temporal aligning. Under this framework, closed form solutions of an EM type learning algorithm are derived. The method can be applied to all types of curve data but is particularly useful when phase variation is present. We demonstrate the method by both simulation studies and an application to human growth curves.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4V2NK6S-4/2/846422ded70f10bf686af860b482b8d5
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 53 (2009)
    Issue (Month): 4 (February)
    Pages: 1361-1376

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    Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1361-1376

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    Web page: http://www.elsevier.com/locate/csda

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    References

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    1. Luis Angel Garcia-Escudero & Alfonso Gordaliza, 2005. "A Proposal for Robust Curve Clustering," Journal of Classification, Springer, vol. 22(2), pages 185-201, September.
    2. Xueli Liu & Hans-Georg Muller, 2004. "Functional Convex Averaging and Synchronization for Time-Warped Random Curves," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 687-699, January.
    3. Jeng-Min Chiou & Pai-Ling Li, 2007. "Functional clustering and identifying substructures of longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 679-699.
    4. Serban, Nicoleta & Wasserman, Larry, 2005. "CATS: Clustering After Transformation and Smoothing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 990-999, September.
    5. Tarpey, Thaddeus, 2007. "Linear Transformations and the k-Means Clustering Algorithm: Applications to Clustering Curves," The American Statistician, American Statistical Association, vol. 61, pages 34-40, February.
    6. Daniel Gervini & Theo Gasser, 2005. "Nonparametric maximum likelihood estimation of the structural mean of a sample of curves," Biometrika, Biometrika Trust, vol. 92(4), pages 801-820, December.
    7. Daniel Gervini & Theo Gasser, 2004. "Self-modelling warping functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 959-971.
    8. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
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    Cited by:
    1. Allou Samé & Faicel Chamroukhi & Gérard Govaert & Patrice Aknin, 2011. "Model-based clustering and segmentation of time series with changes in regime," Advances in Data Analysis and Classification, Springer, vol. 5(4), pages 301-321, December.
    2. 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.
    3. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    4. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.

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