funOCLUST: Clustering Functional Data with Outliers

Katharine M. Clark; Paul D. McNicholas

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arXiv · 2508.00110

funOCLUST: Clustering Functional Data with Outliers

Katharine M. Clark·Paul D. McNicholas

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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.

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Katharine M. Clark, Paul D. McNicholas. 2025-07-31. funOCLUST: Clustering Functional Data with Outliers. https://arxiv.org/abs/2508.00110

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