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Main Authors: Ly, An, Sawhney, Raj, Chugunova, Marina
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2408.07763
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author Ly, An
Sawhney, Raj
Chugunova, Marina
author_facet Ly, An
Sawhney, Raj
Chugunova, Marina
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07763
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm
Ly, An
Sawhney, Raj
Chugunova, Marina
Optimization and Control
Machine Learning
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.
title Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm
topic Optimization and Control
Machine Learning
url https://arxiv.org/abs/2408.07763