funOCLUST: Clustering Functional Data with Outliers
Machine Learning
2025-08-06 v1 Machine Learning
Methodology
Abstract
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identification.
Cite
@article{arxiv.2508.00110,
title = {funOCLUST: Clustering Functional Data with Outliers},
author = {Katharine M. Clark and Paul D. McNicholas},
journal= {arXiv preprint arXiv:2508.00110},
year = {2025}
}