RatioRF: a novel measure for Random Forest clustering based on the Tversky's Ratio model
Manuele Bicego, Ferdinando Cicalese, Antonella Mensi · IEEE Transactions on Knowledge and Data Engineering · 2021
In this paper we propose RatioRF, a novel Random Forest-based similarity measure for clustering. We build upon Tversky's ratio model definition of similarity 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 RatioRF represents a good alternative to other similar measures for clustering recently studied in the literature.