An Improved Adaptive Self-Organizing Map

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 differently 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, confirms the effectiveness of the proposed adaptive SOM.