Clustering multivariate functional data using unsupervised binary trees

•An effective model-based clustering algorithm for multivariate functional data.•The algorithm is recursive and based on a set of binary trees.•The number of clusters is determined in a data-driven way.•New data are easily classified. A model-based clustering algorithm is proposed for a general clas...

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Veröffentlicht in:Computational statistics & data analysis Jg. 168; S. 107376
Hauptverfasser: Golovkine, Steven, Klutchnikoff, Nicolas, Patilea, Valentin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2022
Elsevier
Schlagworte:
algorithms
data analysis
data collection
Gaussian mixtures
Machine Learning
Model-based clustering
Multivariate functional principal components
Statistics
Gaussian mixtures
Model-based clustering
Multivariate functional principal components
Multivariate Functional Principal Components
ISSN:0167-9473, 1872-7352
Online-Zugang:Volltext
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Zusammenfassung:•An effective model-based clustering algorithm for multivariate functional data.•The algorithm is recursive and based on a set of binary trees.•The number of clusters is determined in a data-driven way.•New data are easily classified. A model-based clustering algorithm is proposed for a general class of functional data for which the components could be curves or images. The random functional data realizations could be measured with errors at discrete, and possibly random, points in the definition domain. The idea is to build a set of binary trees by recursive splitting of the observations. The number of groups are determined in a data-driven way. The new algorithm provides easily interpretable results and fast predictions for online data sets. Results on simulated datasets reveal good performance in various complex settings. The methodology is applied to the analysis of vehicle trajectories on a German roundabout.
Bibliographie:ObjectType-Article-1
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2021.107376