We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure that yield compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a weighted graph representation of the input dataset. Such a weighted graph representation is induced by a specific parameter configuration of the dissimilarity measure adopted by an agent for the search. During its lifetime, each agent evaluates different parameter configurations and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems. Notably, we compare our method with respect to several graph-based clustering algorithms and a well-known subspace search method.

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery / Bianchi, FILIPPO MARIA; Maiorino, Enrico; Livi, Lorenzo; Rizzi, Antonello; Sadeghian, Alireza. - In: SOFT COMPUTING. - ISSN 1432-7643. - STAMPA. - 21:5(2017), pp. 1347-1369. [10.1007/s00500-015-1876-1]

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery

BIANCHI, FILIPPO MARIA;MAIORINO, ENRICO;LIVI, LORENZO;RIZZI, Antonello;
2017

Abstract

We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure that yield compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a weighted graph representation of the input dataset. Such a weighted graph representation is induced by a specific parameter configuration of the dissimilarity measure adopted by an agent for the search. During its lifetime, each agent evaluates different parameter configurations and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems. Notably, we compare our method with respect to several graph-based clustering algorithms and a well-known subspace search method.
2017
agent-based algorithms; data mining; knowledge discovery; clustering; local dissimilarity measure; graph conductance; random walk
01 Pubblicazione su rivista::01a Articolo in rivista
An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery / Bianchi, FILIPPO MARIA; Maiorino, Enrico; Livi, Lorenzo; Rizzi, Antonello; Sadeghian, Alireza. - In: SOFT COMPUTING. - ISSN 1432-7643. - STAMPA. - 21:5(2017), pp. 1347-1369. [10.1007/s00500-015-1876-1]
File allegati a questo prodotto
File Dimensione Formato  
Bianchi_Agent-based_2017.pdf

solo utenti autorizzati

Note: An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.44 MB
Formato Adobe PDF
1.44 MB Adobe PDF   Contatta l'autore

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/11573/794065
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 5
social impact