Clustering of functional data in a low-dimensional subspace

Michio Yamamoto

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)219-247
Number of pages29
JournalAdvances in Data Analysis and Classification
Issue number3
Publication statusPublished - Oct 2012
Externally publishedYes


  • Clustering
  • Dimension reduction
  • Functional data
  • Low-dimensional space
  • Smoothing

ASJC Scopus subject areas

  • Statistics and Probability
  • Computer Science Applications
  • Applied Mathematics


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