Functional data clustering using principal curve methods

Ruhao Wu, Bo Wang, Aiping Xu

Research output: Contribution to journalArticlepeer-review


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
Early online date15 Jan 2021
Publication statusE-pub ahead of print - 15 Jan 2021


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

ASJC Scopus subject areas

  • Statistics and Probability


Dive into the research topics of 'Functional data clustering using principal curve methods'. Together they form a unique fingerprint.

Cite this