PyClustrPath: An efficient Python package for generating clustering paths with GPU acceleration
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910801638457344 |
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| author | Wu, Hongfei Yuan, Yancheng |
| author_facet | Wu, Hongfei Yuan, Yancheng |
| contents | Convex clustering is a popular clustering model without requiring the number of clusters as prior knowledge. It can generate a clustering path by continuously solving the model with a sequence of regularization parameter values. This paper introduces {\it PyClustrPath}, a highly efficient Python package for solving the convex clustering model with GPU acceleration. {\it PyClustrPath} implements popular first-order and second-order algorithms with a clean modular design. Such a design makes {\it PyClustrPath} more scalable to incorporate new algorithms for solving the convex clustering model in the future. We extensively test the numerical performance of {\it PyClustrPath} on popular clustering datasets, demonstrating its superior performance compared to the existing solvers for generating the clustering path based on the convex clustering model. The implementation of {\it PyClustrPath} can be found at: https://github.com/D3IntOpt/PyClustrPath. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_15964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyClustrPath: An efficient Python package for generating clustering paths with GPU acceleration Wu, Hongfei Yuan, Yancheng Optimization and Control Convex clustering is a popular clustering model without requiring the number of clusters as prior knowledge. It can generate a clustering path by continuously solving the model with a sequence of regularization parameter values. This paper introduces {\it PyClustrPath}, a highly efficient Python package for solving the convex clustering model with GPU acceleration. {\it PyClustrPath} implements popular first-order and second-order algorithms with a clean modular design. Such a design makes {\it PyClustrPath} more scalable to incorporate new algorithms for solving the convex clustering model in the future. We extensively test the numerical performance of {\it PyClustrPath} on popular clustering datasets, demonstrating its superior performance compared to the existing solvers for generating the clustering path based on the convex clustering model. The implementation of {\it PyClustrPath} can be found at: https://github.com/D3IntOpt/PyClustrPath. |
| title | PyClustrPath: An efficient Python package for generating clustering paths with GPU acceleration |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.15964 |