Clustering Quality and Topology Preservation in Fast Learning SOMs (Articolo in rivista)

Type
Label
  • Clustering Quality and Topology Preservation in Fast Learning SOMs (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Alternative label
  • Di Fatta Giuseppe, Fiannaca Antonino, Gaglio Salvatore, Rizzo Riccardo, Urso Alfonso (2009)
    Clustering Quality and Topology Preservation in Fast Learning SOMs
    in Neural Network World (Prague)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Di Fatta Giuseppe, Fiannaca Antonino, Gaglio Salvatore, Rizzo Riccardo, Urso Alfonso (literal)
Pagina inizio
  • 625 (literal)
Pagina fine
  • 639 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 19-5 (literal)
Rivista
Note
  • Scopus (literal)
  • ISI Web of Science (WOS) (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 2,3,4,5 - ICAR-CNR, ITALY 1, - University of Reading, UK (literal)
Titolo
  • Clustering Quality and Topology Preservation in Fast Learning SOMs (literal)
Abstract
  • The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multi-dimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM. (literal)
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Autore CNR

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