Self organizing maps are generally used in many fields like bio-informatics, neural networks?.It is basically used for the grouping of data. In decision making we reach a conclusion by making certain tests to the input given to the system. We will study the outputs obtained by the tests and reach a solution. Consider a decision making system which checks whether a cloth is wet or not. If we give the parameters of cloth we need to know whether it is wet or not. Simply we can say if moisture is there, the cloth is wet. But that is not the case always. The wet nature can vary. We cannot take specific boundary to distinguish between the wet cloth dry one.?
In SOM we are making some sort of decision-making. Here we will input one data and we will take a set of random points in the space where the data is put. Usually that will be two-dimensional plane. All the points will be having own weight vectors. Those points are called the neurons. Then another algorithm called winner-take over used. Here the distance between the input and each of the neurons are calculated and one is selected according to the minimum distance. Then the input is moved towards the neuron along with its neighborhood. Neighborhood is the set of neurons with in a certain distance of the input. Again the process is continued till the similarity reaches a certain limit, that allows the input to accept the weight of one neuron as its own. This automatic mapping of input to one of the neuron is called the SOM. This technology is used to find the weight of unknown thing.