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funOCLUST: Clustering Functional Data with Outliers
[Submitted on 31 Jul 2025 (v1), last revised 13 Jul 2026 (this v · 2025-08-01 · via stat updates on arXiv.org

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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.

Submission history

From: Katharine Clark [view email]
[v1] Thu, 31 Jul 2025 19:00:20 UTC (234 KB)
[v2] Mon, 13 Jul 2026 14:46:13 UTC (2,427 KB)