Fusion of self organizing maps

Carolina Saavedra, Rodrigo Salas, Sebastián Moreno, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


An important issue in data-mining is to find effective and optimal forms to learn and preserve the topological relations of highly dimensional input spaces and project the data to lower dimensions for visualization purposes. In this paper we propose a novel ensemble method to combine a finite number of Self Organizing Maps, we called this model Fusion-SOM. In the fusion process the nodes with similar Voronoi polygons are merged in one fused node and the neighborhood relation is given by links that measures the similarity between these fused nodes. The aim of combining the SOM is to improve the quality and robustness of the topological representation of the single model. Computational experiments show that the Fusion-SOM model effectively preserves the topology of the input space and improves the representation of the single SOM. We report the performance results using synthetic and real datasets, the latter obtained from a benchmark site.

Original languageEnglish
Title of host publicationComputational and Ambient Intelligence - 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783540730064
StatePublished - 2007
Externally publishedYes
Event9th International Work-Conference on Artificial Neural Networks, IWANN 2007 - San Sebastian, Spain
Duration: 20 Jun 200722 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4507 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Work-Conference on Artificial Neural Networks, IWANN 2007
CitySan Sebastian


  • Machine ensembles
  • Machine fusion
  • Self organizing maps


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