Kohonen Self Organizing Neural Network

Data Science, AI and ML
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The objective of a Kohonen map is to input vectors of arbitrary dimension to discrete map comprised of neurons. The map needs to be trained to create its own organization of the training data. It comprises either one or two dimensions. When training the map the location of the neuron remains constant but the weights differ depending on the value. This self-organization process has different parts, in the first phase, every neuron value is initialized with a small weight and the input vector.
In the second phase, the neuron closest to the point is the ‘winning neuron’ and the neurons connected to the winning neuron will also move towards the point like in the graphic below. The distance between the point and the neurons is calculated by the euclidean distance, the neuron with the least distance wins. Through the iterations, all the points are clustered and each neuron represents each kind of cluster. This is the gist behind the organization of Kohonen Neural Network.
Kohonen Neural Network is used to recognize patterns in the data. Its application can be found in medical analysis to cluster data into different categories. Kohonen map was able to classify patients having glomerular or tubular with an high accuracy. Here is a detailed explanation of how it is categorized mathematically using the euclidean distance algorithm. Below is an image displaying a comparison between a healthy and a diseased glomerular.