An efficient self-organizing map (E-SOM) learning algorithm using group of neurons

In the learning process of the conventional SOM, the neuron which is closer to the winner neuron learns more than the neuron which is farther away from the winner neuron. The neurons farther away from input are not able to learn properly and some dead units are left on the map. To decrease dead unit problem and improve the learning efficiency, an efficient Self-organzing map algorithm using group of neurons has been proposed. In this paper, we have divided the neurons on the map into two groups according to distance from input: normal and distant. The neurons which are far away from the input have been named distant neurons. We have done some changes in the kernel function for the distant neurons and then compared the learning efficiency of the algorithms by applying on standard input dataset. The results have been compared using three well known parameters, which are widely accepted for checking the learning efficiency of machine learning algorithms. It has been observed from the experimental results that proposed SOM successfully decrease dead units, while still preserving the topology of input data with lesser errors. The maps achieved by the proposed SOM have a lower error measure than the maps formed by SOM and false neighbor degree SOM (FN-SOM).