Abstract
We propose a novel adaptive Self-Organizing Map (SOM). In the introduced approach, the SOM neurons’ neighborhood widths are computed adaptively using the information about the frequencies of occurrences of input patterns in the input space. The neighborhood widths are determined independently for each neuron in the SOM grid. In this way, the proposed SOM properly visualizes the input data, especially, when there are significant differences in frequencies of occurrences of input patterns. The experimental study on real data, on three different datasets, verifies and confirms the effectiveness of the proposed adaptive SOM.
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This contribution is supported by the Foundation for Polish Science under International PhD Projects in Intelligent Computing. Project financed from The European Union within the Innovative Economy Operational Programme 2007–2013 and European Regional Development Fund.
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Olszewski, D., Kacprzyk, J., Zadrożny, S. (2016). An Improved Adaptive Self-Organizing Map. In: Trė, G., Grzegorzewski, P., Kacprzyk, J., Owsiński, J., Penczek, W., Zadrożny, S. (eds) Challenging Problems and Solutions in Intelligent Systems. Studies in Computational Intelligence, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-30165-5_5
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