A stability based validity method for fuzzy clustering (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• A stability based validity method for fuzzy clustering (Articolo in rivista) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1016/j.patcog.2009.10.001 (literal)

Alternative label

• Falasconi, M.; Gutierrez, A.; Pardo, M.; Sberveglieri, G., Marco, S. (2010)
  A stability based validity method for fuzzy clustering
  in Pattern recognition
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Falasconi, M.; Gutierrez, A.; Pardo, M.; Sberveglieri, G., Marco, S. (literal)

Pagina inizio

• 1292 (literal)

Pagina fine

• 1305 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 43 (literal)

Rivista

• Pattern recognition (Rivista)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• (Falasconi, Pardo, Sberveglieri) CNR-INFM SENSOR Laboratory and Department of Chemistry and Physics for Engineering and Materials, University of Brescia, Brescia, Italy (Gutierrez, Marco) Departament d'Electrònica, Universitat de Barcelona, Spain and Artificial Olfaction Group, Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain (Pardo) Computational Molecular Biology Department, Max Planck Institute for Molecular Genetics, Berlin, Germany (literal)

Titolo

• A stability based validity method for fuzzy clustering (literal)

Abstract

• An important goal in cluster analysis is the internal validation of results using an objective criterion. Of particular relevance in this respect is the estimation of the optimum number of clusters capturing the intrinsic structure of your data. This paper proposes a method to determine this optimum number based on the evaluation of fuzzy partition stability under bootstrap resampling. The method is first characterized on synthetic data with respect to hyper-parameters, like the fuzzifier, and spatial clustering parameters, such as feature space dimensionality, clusters degree of overlap, and number of clusters. The method is then validated on experimental datasets. Furthermore, the performance of the proposed method is compared to that obtained using a number of traditional fuzzy validity rules based on the cluster compactness-to-separation criteria. The proposed method provides accurate and reliable results, and offers better generalization capabilities than the classical approaches. © 2009 Elsevier Ltd. All rights reserved. (literal)

Prodotto di

• MD.P05.015.001 Materiali nanostrutturati di ossidi metallici e altri semicondutttori per la sensoristica e applicazioni avanzate (MD.P05.015.002) (Modulo)
• Institute of Acoustics and Sensors \"Orso Mario Corbino\" (IDASC) (Istituto)

Autore CNR

• MATTEO PARDO (Persona)
• MATTEO FALASCONI (Unità di personale esterno)
• GIORGIO SBERVEGLIERI (Unità di personale esterno)

Incoming links:

Autore CNR di

• MATTEO PARDO (Persona)
• MATTEO FALASCONI (Unità di personale esterno)
• GIORGIO SBERVEGLIERI (Unità di personale esterno)

Prodotto

• Institute of Acoustics and Sensors \"Orso Mario Corbino\" (IDASC) (Istituto)
• MD.P05.015.001 Materiali nanostrutturati di ossidi metallici e altri semicondutttori per la sensoristica e applicazioni avanzate (MD.P05.015.002) (Modulo)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Pattern recognition (Rivista)