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.

RatioRF: a novel measure for Random Forest clustering based on the Tversky's Ratio model

Bicego, Manuele;Cicalese, Ferdinando;Mensi, Antonella
2021

Abstract

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.
Clustering, Random Forests
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1056480
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