# Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm

arXiv: https://arxiv.org/abs/2408.07763 · HTML: https://mlsanity.com/paper/2408.07763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [A principled methodology for comparing relatedness measures for clustering publications](https://mlsanity.com/paper/1901.06815/) (2019)
- [Unsupervised Key-phrase Extraction and Clustering for Classification Scheme in Scientific Publications](https://mlsanity.com/paper/2101.09990/) (2021)
- [Clustering scientific publications based on citation relations: A systematic comparison of different methods](https://mlsanity.com/paper/1512.09023/) (2015)

## Abstract (arXiv)

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.