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Torus data are multivariate circular observations that arise as measurements on a periodic scale and are often recorded as angles or directions. In this paper, a model based clustering technique for torus data is built on the mclust approach. Therefore, covariance constraints are imposed on the completely general heterogeneous clustering model allowing a flexible and general framework to clustering torus data. The methodology is based on unwrapping the torus data to linear data.

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Constrained and Parsimonious Mixture Models for Multivariate Circular Data

  • 会议论文

  • 发表日期：

    2025年

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  • Antonio Lucadamo,
  • Luca Greco,
  • Claudio Agostinelli

摘要

Torus data are multivariate circular observations that arise as measurements on a periodic scale and are often recorded as angles or directions. In this paper, a model based clustering technique for torus data is built on the mclust approach. Therefore, covariance constraints are imposed on the completely general heterogeneous clustering model allowing a flexible and general framework to clustering torus data. The methodology is based on unwrapping the torus data to linear data.