Abstract
In the present work we showed that together with improved stability the Intrinsic Plasticity (IP) tuned Echo State Network (ESN) reservoirs possess also better clustering abilities that opens a possibility for application of ESNs in multidimensional data clustering. The revealed ability of ESNs is demonstrated first on an artificially created data set with known in advance number and position of clusters. Automated procedure for multidimensional data clustering was proposed. It allows discovering multidimensional data structure without specification in advance the clusters number. The developed procedure was further applied to a real data set containing concentrations of three alloying elements in numerous steel compositions. The obtained number and position of clusters showed logical from the practical point of view data separation.
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Koprinkova-Hristova, P., Tontchev, N. (2012). Echo State Networks for Multi-dimensional Data Clustering. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_72
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DOI: https://doi.org/10.1007/978-3-642-33269-2_72
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