## Clustering of functional data in a low-dimensional subspace.(English)Zbl 1254.62077

Summary: 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 to 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 of 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.

### MSC:

 62H30 Classification and discrimination; cluster analysis (statistical aspects) 65C60 Computational problems in statistics (MSC2010)

### Keywords:

dimension reduction; smoothing

### Software:

funHDDC; fda (R); R
Full Text:

### References:

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