Depth-based clustering analysis of directional data
Methodology
2022-06-22 v1
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.
Cite
@article{arxiv.2206.10447,
title = {Depth-based clustering analysis of directional data},
author = {Giuseppe Pandolfo and Antonio D'ambrosio},
journal= {arXiv preprint arXiv:2206.10447},
year = {2022}
}