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.
Clustering directional data through depth functions / Pandolfo, Giuseppe; D’Ambrosio, Antonio. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 38:(2023), pp. 1487-1506. [10.1007/s00180-022-01281-w]
Clustering directional data through depth functions
Giuseppe Pandolfo
Conceptualization
;Antonio D’ambrosioConceptualization
2023
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
