Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm
<|reference_start|>Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm: 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.<|reference_end|>
@article{ly2024data,
    title={Data Clustering and Visualization with Recursive Goemans-Williamson
  MaxCut Algorithm},
    author={An Ly, Raj Sawhney, Marina Chugunova},
    journal={arXiv preprint arXiv:2408.07763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07763},
    primaryClass={math.OC cs.LG}
}