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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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Data Clustering and Visualization with Recursive Max k-Cut Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • An Ly,
  • Raj Sawhney,
  • Marina Chugunova

摘要

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.