deploying SOM approaches are compared with the results as reported. The comparisons are given in the table 3 in Appendices.

## 3. Overview Of Self Organizing Map **Learning Algorithm**

Artificial Neural Networks (ANN) is computer algorithm, inspired by the functioning of the nervous system of the human brain, capable of learning from data and generalizing. 

This learning process can be described as supervised or unsupervised learning. In the supervised learning process, the ANN is shown several input-output patterns during training to enable the trained ANN to make generalizations based on the training data and to correctly produce output patterns based on new input (Jain et al., 1996). The SOM-
algorithm is based on unsupervised learning, which means that the desired output is not known a priori. The goal of the learning process is not to make predictions, but to classify data according to their similarity. In the neural network architecture Kohonen proposed (Kohonen, 1998), the classification is done by plotting the data in n-dimensions onto a, usually, two-dimensional grid of units in a topology preserving manner. The former means that similar observations are plotted in each others neighborhood on the 2-D-grid. The neural network consists of an input layer and a layer of neurons. The neurons or units are arranged on a rectangular or hexagonal grid and are fully interconnected. Each of the input vectors is also connected to each of the units. The learning algorithm applied to the network (Kohonen, 2001; Kaski, 1997): An input vector is shown to the network; the Euclidean distances between the considered input vector xi and all of the reference vectors mi are calculated.

$$x_{i}=\left[x_{1},x_{2},...,x_{n}\right]T\in\Re^{n},i=1,2,...,N\qquad\qquad...(1)$$
$$_{i}=\left[m_{1},m_{2},...m_{n}\right]T\in\Re^{n},i=1,2,...,N\qquad\qquad...Q)$$

The connection between the two layers represents a map of real high-dimensional data onto a low-dimensional (usually 2-D) display of the nodes. In the training process, The best matching unit mc, the unit with the greatest similarity with the considered input vector, is chosen according to:
x m min  x - m  ...(3)
c i i
 
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