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Application of SOM neural network in clustering

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DOI: 10.4236/jbise.2009.28093    4,701 Downloads   10,347 Views   Citations


The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to analyze the SOM in clustering different species of animals.

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The authors declare no conflicts of interest.

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Behbahani, S. and Nasrabadiv, A. (2009) Application of SOM neural network in clustering. Journal of Biomedical Science and Engineering, 2, 637-643. doi: 10.4236/jbise.2009.28093.


[1] A. Forti, (2006) Growing hierarchical tree SOM: An unsupervised neural network with dynamic topology, , Gian Luca Foresti, Neural Networks, 19, 1568–1580.
[2] S. Haykin, (1999) Neural networks a comprehensive foundation (2nd ed.), Prentice Hall.
[3] R. G. Adams, K. Butchart and N. Davey, (1999) Hierarchical classification with a competitive evolutionary neural tree, Neural Networks, 12, 541–551.
[4] J. Li, Information visualization of self organizing maps.

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