An Approach to Find the Serotypes of Rotavirus Using Self-Organizing Feature Map
DOI:
https://doi.org/10.26438/ijcse/v6i10.96100Keywords:
SOFM, ANN, Neurons, Cluster, Classification, Quantization, VisualizationAbstract
Self-organizing maps (SOM) are different from other artificial neural networks in the sense that they use a neighbourhood function to preserve the topological properties of the input space. It converts complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. In this works, classifying the virus type using the SOM Toolbox. Self-organizing feature maps (SOFM) learn to classify input vectors according to how they are grouped in the input space. They differ from competitive layers in that neighboring neurons in the self-organizing map learn to recognize neighbouring sections of the input space
References
[1] T. Kohonen, “The self-organizing map”, In the Proceedings of the IEEE, 78, pp. 1464–1480, 1990.
[2] A. Ultsch, “Self-Organizing Neural Networks for Visualization and Classification”, In: O. Opitzet al. (Eds). Information and Classification, Springer Berlin, pp. 301–306, 1993.
[3] J. Vesanto, “Using SOM in Data Mining”, In Licentiate`s Paper, Helsinki University of Technology, Espoo, Finland, 2000.
[4] J. A. F. Costa and M. L. de Andrade Netto, “Clustering of complex shaped data sets via Kohonen maps and mathematical morphology”, In: B. Dasarathy (Ed.), Proceedings of the SPIE, Data Mining and Knowledge Discovery, 4384, pp. 16-27, 2001.
[5] F. L. Gorgônio and J. A. F. Costa, “Parallel Self-Organizing Maps with Applications in Clustering Distributed Data”, In the International Joint Conference on Neural Networks (IJCNN’2008), 2008.
[6] S. Haykin, “Neural networks: A comprehensive foundation”, 2nd ed., Macmillan College Publishing Company, New York, 1999.
[7] T. Kohonen, “Emergence of invariant-feature de-tectors in the adaptive-subspace self-organizing map”, Biological Cybernetics, Vol.75, pp.281-291, 1996.
[8] T. Kohonen, “Self-Organizing Map”, Springer-Verlag, 1995.
[9] R. Xu and D. Wunsch II, “Survey of Clustering Algorithms”, IEEE Trans. on Neural Networks, Vol.16, Issue. 3, pp. 645-678, 2005.
[10] F. L. Gorgônio and J. A. F. Costa, “Parallel Self-Organizing Maps with Applications in Clustering Distributed Data”, In International Joint Conference on Neural Networks (IJCNN’2008), 2008.
[11] P. Berkhin, “A Survey of Clustering Data Mining Techniques”, In: J. Kogan et al., Grouping Multi-dimensional Data: Recent Advances in Clustering. Springer-Verlag, New York, 2006.
[12] T. Francis Thamburaj, “Analysis of Genome Signature Strength of SARS Coronavirus som neural network”, IEEE, 2010.
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