Functional data clustering via hypothesis testing \(k\)-means

DOI10.1007/s00180-018-0808-9zbMath1417.62181OpenAlexW2793566517MaRDI QIDQ2418054

Julian A. A. Collazos, Adriano Zanin Zambom, Ronaldo Dias

Publication date: 3 June 2019

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-018-0808-9

zbMATH Keywords

parallelismANOVAB-splines\(t\) testtest-based \(k\)-means algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (6)

A joint latent factor analyzer and functional subspace model for clustering multivariate functional data ⋮ Editorial on the special issue on functional data analysis and related topics ⋮ Supervised classification of curves via a combined use of functional data analysis and tree-based methods ⋮ A fast epigraph and hypograph-based approach for clustering functional data ⋮ Interval-valued functional clustering based on the Wasserstein distance with application to stock data ⋮ Clustering multivariate functional data in group-specific functional subspaces

Uses Software

• fda (R)
• funHDDC
• fda.usc

Cites Work

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