Abstract
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.
This work was supported by Research Grant Fondecyt 1061201, 1070220 and DGIP-UTFSM.
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Saavedra, C., Salas, R., Moreno, S., Allende, H. (2007). Fusion of Self Organizing Maps. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_28
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DOI: https://doi.org/10.1007/978-3-540-73007-1_28
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