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Clustering of functional data in a low-dimensional subspace

  • Michio Yamamoto

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    To find optimal clusters of functional objects in a lower-dimensional subspace of data, a sequential method called tandem analysis, is often used, though such a method is problematic. A new procedure is developed to find optimal clusters of functional objects and also find an optimal subspace for clustering, simultaneously. The method is based on the k-means criterion for functional data and seeks the subspace that is maximally informative about the clustering structure in the data. An efficient alternating least-squares algorithm is described, and the proposed method is extended to a regularized method. Analyses of artificial and real data examples demonstrate that the proposed method gives correct and interpretable results. Copyright Springer-Verlag 2012

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    File URL: http://hdl.handle.net/10.1007/s11634-012-0113-3
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    Article provided by Springer in its journal Advances in Data Analysis and Classification.

    Volume (Year): 6 (2012)
    Issue (Month): 3 (October)
    Pages: 219-247

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    Handle: RePEc:spr:advdac:v:6:y:2012:i:3:p:219-247
    Contact details of provider: Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634

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