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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)

Software:

funHDDC; fda (R); R
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References:

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