Clustering directional data through depth functions

Giuseppe Pandolfo () and Antonio D’ambrosio
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Giuseppe Pandolfo: University of Naples Federico II
Antonio D’ambrosio: University of Naples Federico II

Computational Statistics, 2023, vol. 38, issue 3, No 17, 1487-1506

Abstract: 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.

Keywords: Spherical random variables; Spherical distance; Textual data; von Mises-Fisher (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01281-w

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