Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data

Marzia A. Cremona, Francesca Chiaromonte

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We develop a new method to locally cluster curves and discover functional motifs, that is, typical shapes that may recur several times along and across the curves capturing important local characteristics. In order to identify these shared curve portions, our method leverages ideas from functional data analysis (joint clustering and alignment of curves), bioinformatics (local alignment through the extension of high similarity seeds) and fuzzy clustering (curves belonging to more than one cluster, if they contain more than one typical shape). It can employ various dissimilarity measures and incorporate derivatives in the discovery process, thus exploiting complex facets of shapes. We demonstrate the performance of our method with an extensive simulation study, and show how it generalizes other clustering methods for functional data. Finally, we provide real data applications to Italian Covid-19 death curves and Omics data related to mutagenesis. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1119-1130
Number of pages12
JournalJournal of Computational and Graphical Statistics
Volume32
Issue number3
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data'. Together they form a unique fingerprint.

Cite this