In this paper we present DisRFC (Dissimilarity Random Forest Clustering), a novel Random Forest Clustering approach which, contrarily to current methods which require in input a vectorial representation, works only with dissimilarities, thus being applicable also to all those problems where a vectorial representation is not available but a descriptive dissimilarity measure can be computed. In the DisRFC approach objects to be clustered are first modelled with a novel RF variant called Unsupervised Dissimilarity Random Forest (UD-RF), which functioning mechanisms are both unsupervised and based on dissimilarities. The trained UD-RF is then used to project objects in a binary vectorial space, where effective K-means procedures can be used to obtain the final clustering. In the paper we present different variants of DisRFC, thoroughly and positively evaluated using 10 different problems.

Dissimilarity Random Forest Clustering

Bicego, M
2020-01-01

Abstract

In this paper we present DisRFC (Dissimilarity Random Forest Clustering), a novel Random Forest Clustering approach which, contrarily to current methods which require in input a vectorial representation, works only with dissimilarities, thus being applicable also to all those problems where a vectorial representation is not available but a descriptive dissimilarity measure can be computed. In the DisRFC approach objects to be clustered are first modelled with a novel RF variant called Unsupervised Dissimilarity Random Forest (UD-RF), which functioning mechanisms are both unsupervised and based on dissimilarities. The trained UD-RF is then used to project objects in a binary vectorial space, where effective K-means procedures can be used to obtain the final clustering. In the paper we present different variants of DisRFC, thoroughly and positively evaluated using 10 different problems.
2020
978-1-7281-8316-9
Random Forest clustering
Renyi divergence
Unsupervised Random Forest
Ensemble clustering
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1086947
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact