Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm

Autor:	Ly, An, Sawhney, Raj, Chugunova, Marina
Rok vydání:	2024
Předmět:	Mathematics - Optimization and Control Computer Science - Machine Learning
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
Externí odkaz:	http://arxiv.org/abs/2408.07763 Zobrazit plný text záznamu View this record from Arxiv