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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07771 |
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| _version_ | 1866929459997704192 |
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| author | Ly, An Sawhney, Raj Chugunova, Marina |
| author_facet | Ly, An Sawhney, Raj Chugunova, Marina |
| contents | In this article, we continue our analysis for a novel recursive modification to the Max $k$-Cut algorithm using semidefinite programming as its basis, offering an improved performance in vectorized data clustering tasks. Using a dimension relaxation method, we use a recursion method to enhance density of clustering results. Our methods provide advantages in both computational efficiency and clustering accuracy for grouping datasets into three clusters, substantiated through comprehensive experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Clustering and Visualization with Recursive Max k-Cut Algorithm Ly, An Sawhney, Raj Chugunova, Marina Optimization and Control In this article, we continue our analysis for a novel recursive modification to the Max $k$-Cut algorithm using semidefinite programming as its basis, offering an improved performance in vectorized data clustering tasks. Using a dimension relaxation method, we use a recursion method to enhance density of clustering results. Our methods provide advantages in both computational efficiency and clustering accuracy for grouping datasets into three clusters, substantiated through comprehensive experiments. |
| title | Data Clustering and Visualization with Recursive Max k-Cut Algorithm |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2408.07771 |