Functional data clustering using principal curve methods

Ruhao Wu, Bo Wang, Aiping Xu

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Abstract

In this paper we propose a novel clustering method for functional data based on the principal curve clustering approach. By this method functional data are approximated using functional principal component analysis (FPCA) and the principal curve clustering is then performed on the principal scores. The proposed method makes use of the nonparametric principal curves to summarize the features of the principal scores extracted from the original functional data, and a probabilistic model combined with Bayesian Information Criterion is employed to automatically and simultaneously find the appropriate number of features, the optimal degree of smoothing and the corresponding cluster members. The simulation studies show that the proposed method outperforms the existing functional clustering approaches considered. The capability of this method is also demonstrated by the applications in the human mortality and fertility data.
Original languageEnglish
Pages (from-to)(In-Press)
JournalCommunications in Statistics - Theory and Methods
Volume(In-Press)
Early online date15 Jan 2021
DOIs
Publication statusE-pub ahead of print - 15 Jan 2021

Bibliographical note

This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Theory and Methods, on 15/01/2021, available online: http://www.tandfonline.com/ 10.1080/03610926.2021.1872636

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders

Keywords

  • Clustering
  • functional data analysis
  • functional principal component analysis
  • principal curves

ASJC Scopus subject areas

  • Statistics and Probability

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