← 研踪 PaperTrailarXiv:2508.00110 · v3
funOCLUST: Clustering Functional Data with Outliers
首次提交 2025-08-01 03:00
版本更新 2026-11-07 06:08
雷达观测 2026-11-10 11:14
摘要
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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v3 · 2026-11-07 06:08 · 最新观测版本
funOCLUST: Clustering Functional Data with Outliers
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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