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<p>A non-Markovian framework for detecting and modelling observations that change groups across variables is introduced. This framework takes advantage of the concept of missing data, allowing straightforward implementation using the expectation-maximization algorithm for Gaussian mixture models. Importantly, the components used to model group-switching behaviour require only one additional free parameter to be estimated: making it a feasible approach for detecting and modelling rare group-switching events. We motivate the concepts underlying the techniques through simple examples, provide details for the model-fitting algorithms, and discuss results on real data.</p>

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Modelling Group-Switching in Cluster Analysis via Compositional Mixture Components

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Liam Welsh,
  • Jeffrey L. Andrews,
  • Ryan Browne

摘要

A non-Markovian framework for detecting and modelling observations that change groups across variables is introduced. This framework takes advantage of the concept of missing data, allowing straightforward implementation using the expectation-maximization algorithm for Gaussian mixture models. Importantly, the components used to model group-switching behaviour require only one additional free parameter to be estimated: making it a feasible approach for detecting and modelling rare group-switching events. We motivate the concepts underlying the techniques through simple examples, provide details for the model-fitting algorithms, and discuss results on real data.