Clustering Quality and Topology Preservation in Fast Learning SOMs

Salvatore Gaglio, Alfonso Urso, Giuseppe Di Fatta, Riccardo Rizzo, Antonino Fiannaca

Risultato della ricerca: Articlepeer review

3 Citazioni (Scopus)

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 multidimensional 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.
Lingua originaleEnglish
pagine (da-a)625-639
Numero di pagine15
RivistaNeural Network World
Volume19
Stato di pubblicazionePublished - 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Hardware and Architecture
  • Artificial Intelligence

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