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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/2408.07763 |
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| _version_ | 1866913468580364288 |
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| author | Ly, An Sawhney, Raj Chugunova, Marina |
| author_facet | Ly, An Sawhney, Raj Chugunova, Marina |
| contents | In this article, we introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, we employ recursive iterations in conjunction with a dimension relaxation method to significantly enhance density of clustering results. Furthermore, we propose a unique vectorization technique for articles, leveraging conditional probabilities for more effective clustering. Our methods provide advantages in both computational efficiency and clustering accuracy, substantiated through comprehensive experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm Ly, An Sawhney, Raj Chugunova, Marina Optimization and Control Machine Learning In this article, we introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, we employ recursive iterations in conjunction with a dimension relaxation method to significantly enhance density of clustering results. Furthermore, we propose a unique vectorization technique for articles, leveraging conditional probabilities for more effective clustering. Our methods provide advantages in both computational efficiency and clustering accuracy, substantiated through comprehensive experiments. |
| title | Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2408.07763 |