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Clustering of functional data in a low-dimensional subspace

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  • Michio Yamamoto

Abstract

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

Suggested Citation

  • Michio Yamamoto, 2012. "Clustering of functional data in a low-dimensional subspace," 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. 6(3), pages 219-247, October.
    • Handle: RePEc:spr:advdac:v:6:y:2012:i:3:p:219-247
    DOI: 10.1007/s11634-012-0113-3
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    References listed on IDEAS

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