Abstract

In this paper we compare two algorithms that are capable of generating fuzzy partitions from data so as to verify a number of interpretability constraints: Hierarchical Fuzzy Partitioning (HFP) and Double Clustering with A* (DC*). Both algorithms exhibit the distinguishing feature of self-determining the number of fuzzy sets in each fuzzy partition, thus relieving the user from the selection of the best granularity level for each input feature. However, the two algorithms adopt very different approaches in generating fuzzy partitions, thus motivating an extensive experimentation to highlight points of strength and weakness of both. The experimental results show that, while HFP is on the average more efficient, DC* is capable of generating fuzzy partitions with a better trade-off between interpretability and accuracy, and generally offers greater stability with respect to its hyper-parameters.

Autore Pugliese

• Fanelli Anna Maria , Castiello Ciro , Mencar Corrado

Tutti gli autori

• FANELLI A.M.;CASTIELLO C.;MENCAR C.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2013

ISSN

Non Disponibile

ISBN

978-3-642-38657-2

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile