Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm
Autor: | Ly, An, Sawhney, Raj, Chugunova, Marina |
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Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Published in the IEEE Conference, CSCI 2023 (Winter Session) |
Databáze: | arXiv |
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