SELF ORGANIZING MAPS

 Nandini D
5th BCA NCMS

 SOM is developed by Finnish professor and researcher Dr. TeuvoKohonen in 1982 was sometimes called a Kohonen map. It is a type of artificial neural network which follows unsupervised learning to generate a low dimensional (two dimensional), discretized representation of the input space of the training samples are referred as map, and is therefore a method to do dimensionality reduction. They apply competitive learning instead as opposed to error correction learning.

Working:

                     The data points in the data set recognize themselves by competing for themselves. Initializing the weight vectors is the first step of mapping in SOM. A weight that best represents that sample is searched from the randomly selected sample vector and the map of the weight vectors. The weight vectors have neighboring weights that are close to it. The chosen weight  and the neighboring weights are rewarded to become more like that randomly selected sample vector. This will allow the SOM to take different shapes. Commonly they form square/rectangle/hexagonal/L shape/in 2D feature space.

Algorithm:

                    For 0 to X number of training epochs

                           Select a sample from the input set

                           Find the “winning”  neuron for the sample input

                           Adjust the weights of nearby neurons

                   End for loop

Pros:

●       Data is interpreted and understood easily.

●       Dimensional reduction helps in observing similarities in data.

Cons:

●       Extraneous data in weight vectors will add randomness to the groups.

For more information about the SOM do watch Click the link below

video:https://www.youtube.com/watch?v=0qtvb_Nx2tA