funOCLUST: Clustering Functional Data with Outliers
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911092857372672 |
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| author | Clark, Katharine M. McNicholas, Paul D. |
| author_facet | Clark, Katharine M. McNicholas, Paul D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00110 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | funOCLUST: Clustering Functional Data with Outliers Clark, Katharine M. McNicholas, Paul D. Machine Learning Methodology 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. |
| title | funOCLUST: Clustering Functional Data with Outliers |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2508.00110 |