Abstract
In this work we study the effectiveness of the Fuzzy Kohonen Clustering Network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules. The algorithm combines Kohonen’s Self-Organizing Feature Maps (SOM) and Fuzzy c-means clustering technique (FCM) in order to obtain a clustering technique that inherits their best properties. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with Self-Organizing Maps (SOM) and manual classification. Experimental results have proved that this new technique is suitable for working with large, high dimensional and noisy data sets.
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© 2000 Springer-Verlag London
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Pascual, A., Barcéna, M., Merelo, J.J., Carazo, JM. (2000). Self-Organizing Networks for Mapping and Clustering Biological Macromolecules Images. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_43
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_43
Publisher Name: Springer, London
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