funOCLUST: Clustering Functional Data with Outliers

• URL: http://arxiv.org/abs/2508.00110v1
• Date: Thu, 31 Jul 2025 19:00:20 GMT
• Title: funOCLUST: Clustering Functional Data with Outliers
• Authors: Katharine M. Clark, Paul D. McNicholas,
• Abstract summary: Functional data presents unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers.<n>An extension of the OCLUST algorithm to the functional setting is proposed to address these issues.<n>The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers.
• Score: 1.0435741631709405
• License: http://creativecommons.org/licenses/by/4.0/
• 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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