Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm

TL;DR

This paper presents a recursive modification of the Goemans-Williamson MaxCut algorithm that improves data clustering and visualization, especially for medical publication datasets, by enhancing accuracy and efficiency.

Contribution

The paper introduces a novel recursive approach and vectorization technique for MaxCut-based clustering, improving performance and density in medical publication clustering tasks.

Findings

Enhanced clustering density and accuracy

Improved computational efficiency

Effective vectorization for article clustering

Abstract

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.