Abstract
The paper describes a unified approach to solve clustering and classification problems by means of oscillatory neural networks with chaotic dynamics. It is discovered that self-synchronized clusters once formed can be applied to classify objects. The advantages of distributed clusters formation in comparison to centers of clusters estimation are demonstrated. New approach to clustering on-the-fly is proposed.
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Benderskaya, E.N., Zhukova, S.V. (2011). Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19934-9_54
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DOI: https://doi.org/10.1007/978-3-642-19934-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19933-2
Online ISBN: 978-3-642-19934-9
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