Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm
This work addresses clustering challenges for medical publication data, but it appears incremental as it builds on an existing algorithm.
The paper tackled the problem of clustering medical publications by introducing a recursive modification to the Goemans-Williamson MaxCut algorithm, resulting in improved clustering density and accuracy, with advantages in computational efficiency as shown in experiments.
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.