Data Clustering and Visualization with Recursive Max k-Cut Algorithm

An Ly; Raj Sawhney; Marina Chugunova

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arXiv · 2408.07771

Data Clustering and Visualization with Recursive Max k-Cut Algorithm

An Ly·Raj Sawhney·Marina Chugunova

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Abstract

In this article, we continue our analysis for a novel recursive modification to the Max $k$-Cut algorithm using semidefinite programming as its basis, offering an improved performance in vectorized data clustering tasks. Using a dimension relaxation method, we use a recursion method to enhance density of clustering results. Our methods provide advantages in both computational efficiency and clustering accuracy for grouping datasets into three clusters, substantiated through comprehensive experiments.

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BibTeX RIS

An Ly, Raj Sawhney, Marina Chugunova. 2024-08-14. Data Clustering and Visualization with Recursive Max k-Cut Algorithm. https://arxiv.org/abs/2408.07771

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