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<p>In this paper, we introduce a two-stage partitioning clustering procedure based on local depths. In the first stage, we find clusters of the local depth inner region of level <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\alpha .\)</EquationSource> </InlineEquation> In the second stage, the remaining points are assigned to one of these clusters according to a proximity criterion. In this way, the clusters found in the first stage play the role of flexible centers, that aim to mimic the shape of the groups. We analyze the performance of the procedure on multivariate and multivariate functional data, on real and synthetic datasets showing remarkable results.</p>

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A local depth based clustering procedure

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Lucas Fernández-Piana,
  • Marcela Svarc

摘要

In this paper, we introduce a two-stage partitioning clustering procedure based on local depths. In the first stage, we find clusters of the local depth inner region of level \(\alpha .\) In the second stage, the remaining points are assigned to one of these clusters according to a proximity criterion. In this way, the clusters found in the first stage play the role of flexible centers, that aim to mimic the shape of the groups. We analyze the performance of the procedure on multivariate and multivariate functional data, on real and synthetic datasets showing remarkable results.