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Clustering directional data through depth functions

Author

Listed:
  • Giuseppe Pandolfo

    (University of Naples Federico II)

  • Antonio D’ambrosio

    (University of Naples Federico II)

Abstract

A new depth-based clustering procedure for directional data is proposed. Such method is fully non-parametric and has the advantages to be flexible and applicable even in high dimensions when a suitable notion of depth is adopted. The introduced technique is evaluated through an extensive simulation study. In addition, a real data example in text mining is given to explain its effectiveness in comparison with other existing directional clustering algorithms.

Suggested Citation

  • Giuseppe Pandolfo & Antonio D’ambrosio, 2023. "Clustering directional data through depth functions," Computational Statistics, Springer, vol. 38(3), pages 1487-1506, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-022-01281-w
    DOI: 10.1007/s00180-022-01281-w
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    References listed on IDEAS

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