Multiscale clustering for functional data

DOI10.1007/S00357-019-09313-9zbMath1436.62271OpenAlexW2933588070MaRDI QIDQ2304079

Hee-Seok Oh, Yaeji Lim, Ying-Kuen Cheung

Publication date: 6 March 2020

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-019-09313-9

zbMATH Keywords

multiresolution analysiswavelet transformhigh-dimensional datafunctional dataempirical mode decomposition

Mathematics Subject Classification ID

Functional data analysis (62R10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Statistical aspects of big data and data science (62R07)

Uses Software

• sparcl

Cites Work

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• Sparse clustering of functional data
• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• A comparison of regression spline smoothing procedures
• Improved smoothing spline regression by combining estimates of different smoothness
• Functional data clustering: a survey
• Wavelet-based Functional Mixed Models
• Functional Clustering by Bayesian Wavelet Methods
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Clustering for Sparsely Sampled Functional Data
• CLUSTERING FUNCTIONAL DATA USING WAVELETS
• Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension
• Functional Clustering and Identifying Substructures of Longitudinal Data
• A Framework for Feature Selection in Clustering

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