A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data

DOI10.48550/arXiv.1708.00386zbMath1427.62054arXiv1708.00386OpenAlexW2743292618WikidataQ128845244 ScholiaQ128845244MaRDI QIDQ151074

Anna M. Paganoni, Andrea Martino, Andrea Ghiglietti, Francesca Ieva, Andrea Martino, Francesca Ieva, Andrea Ghiglietti, Anna Maria Paganoni

Publication date: 1 August 2017

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.00386

zbMATH Keywords

correlation\(k\)-means algorithmMahalanobis distancemultivariate functional datadistances in \(L^2\)

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (7)

A joint latent factor analyzer and functional subspace model for clustering multivariate functional data ⋮ Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma ⋮ 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 ⋮ Regression and subgroup detection for heterogeneous samples ⋮ A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data ⋮ gmfd

Uses Software

• R
• fda (R)
• funFEM
• Funclustering
• clusfind
• gmfd

Cites Work

• A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data
• Statistical inference for stochastic processes: two-sample hypothesis tests
• Inference for functional data with applications
• Simultaneous curve registration and clustering for functional data
• Core shape modelling of a set of curves
• \(k\)-mean alignment for curve clustering
• Applied functional data analysis. Methods and case studies
• Model-based clustering for multivariate functional data
• Clustering functional data
• Exact tests for the means of Gaussian stochastic processes
• On \(K\)-means algorithm with the use of Mahalanobis distances
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Finding Groups in Data

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