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.
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Yamamoto, M. Clustering of functional data in a low-dimensional subspace. Adv Data Anal Classif 6, 219–247 (2012). https://doi.org/10.1007/s11634-012-0113-3
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DOI: https://doi.org/10.1007/s11634-012-0113-3