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Abstract <p>This article presents a method for reducing the search space of clustering parameters. This is achieved by selecting the most appropriate data transformation methods and dissimilarity measures at the stage preceding the actual execution of clustering. To compare the selected methods, it is proposed to use the silhouette coefficient, which considers class labels from a small labeled dataset as cluster labels. The results of experimental validation of the proposed approach for clustering news texts are presented.</p>

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Reducing the Search Space for Optimal Clustering Parameters Using a Small Amount of Labeled Data

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • V. I. Yuferev,
  • N. A. Razin

摘要

Abstract

This article presents a method for reducing the search space of clustering parameters. This is achieved by selecting the most appropriate data transformation methods and dissimilarity measures at the stage preceding the actual execution of clustering. To compare the selected methods, it is proposed to use the silhouette coefficient, which considers class labels from a small labeled dataset as cluster labels. The results of experimental validation of the proposed approach for clustering news texts are presented.