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Functional data clustering: a survey

Julien Jacques 1, 2 Cristian Preda 1, 2 
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : The main contributions to functional data clustering are reviewed. Most approaches used for clustering functional data are based on the following three methodologies: dimension reduction before clustering, nonparametric methods using specific distances or dissimilarities between curves and model-based clustering methods. These latter assume a probabilistic distribution on either the principal components or coefficients of functional data expansion into a finite dimensional basis of functions. Numerical illustrations as well as a software review are presented.
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Submitted on : Tuesday, January 8, 2013 - 9:07:15 AM
Last modification on : Tuesday, November 22, 2022 - 2:26:13 PM
Long-term archiving on: : Tuesday, April 9, 2013 - 3:50:36 AM


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Julien Jacques, Cristian Preda. Functional data clustering: a survey. Advances in Data Analysis and Classification, 2014, 8 (3), pp.24. ⟨10.1007/s11634-013-0158-y⟩. ⟨hal-00771030⟩



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