RatioRF: A Novel Measure for Random Forest Clustering Based on the Tversky's Ratio Model

Manuele Bicego, Ferdinando Cicalese, Antonella Mensi

Abstract: In this paper we propose <inline-formula><tex-math notation="LaTeX">${\mathrm{RatioRF}}$</tex-math></inline-formula> , a novel Random Forest-based similarity measure for clustering. We build upon Tversky’s ratio model definition of similarity [1] and specialize it to the Random Forest case. We study some properties of the proposed axiomatic similarity measure and present an extensive experimental clustering analysis involving different datasets and configurations. Results confirm that <inline-formula><tex-math notation="LaTeX">${\mathrm{RatioRF}}$</tex-math></inline-formula> represents a good alternative to other similar measures for clustering recently studied in the literature.