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This research explores the application of Probabilistic Distance Clustering (PDC) to compositional data, specifically addressing the challenge of handling a bounded sample space in distance computations. It first reviews existing log-ratio transformations that map compositions into Euclidean space for clustering. Then, it adapts PDC to handle compositional data by using the additive ratio combined with the Box-Cox transformation. A simulation study will evaluate this method compared to the well-established isometric log-ratio transformation.

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Probabilistic Distance Clustering for Compositional Data

  • 会议论文

  • 发表日期：

    2025年

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  • Violetta Simonacci,
  • Cristina Tortora,
  • Francesco Palumbo,
  • Michele Gallo

摘要

This research explores the application of Probabilistic Distance Clustering (PDC) to compositional data, specifically addressing the challenge of handling a bounded sample space in distance computations. It first reviews existing log-ratio transformations that map compositions into Euclidean space for clustering. Then, it adapts PDC to handle compositional data by using the additive ratio combined with the Box-Cox transformation. A simulation study will evaluate this method compared to the well-established isometric log-ratio transformation.