Data Clustering and Visualization with Recursive Max k-Cut Algorithm
| Autor: | Ly, An, Sawhney, Raj, Chugunova, Marina |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: IEEE CSCE Conference from July 22 to July 25, 2024 |
| Databáze: | arXiv |
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