Fusion of Kohonen Maps Ranked by Cluster Validity Indexes

  • Leandro Antonio Pasa
  • José Alfredo F. Costa
  • Marcial Guerra de Medeiros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)


In this study, a new approach to Kohonen Self-Organizing Maps fusion is presented: the use of modified cluster validity indexes as a criterion for merging Kohonen Maps. Computational simulations were performed with traditional dataset from the UCI Machine Learning Repository, with variations in map size, number of subsets to be merged and the percentage of dataset bagging. The fusion results were compared with a regular single Kohonen Map. In some selected parameters, the proposed method achieves a better accuracy measure.


Fusion Self Organizing Maps Validity Index 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Leandro Antonio Pasa
    • 1
    • 2
  • José Alfredo F. Costa
    • 2
  • Marcial Guerra de Medeiros
    • 2
  1. 1.Federal Technological University of Paraná, UTFPRMedianeiraBrazil
  2. 2.Federal University of Rio Grande do Norte, UFRNNatalBrazil

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