How does the map creation work?
The SOM algorithm starts out in the space spanned by the two largest principal component eigenvectors. The nodes are evenly distributed over this plane and initialized with the corresponding values. The data records (also called input vectors) will be matched to the node with the shortest Euclidean distance (i.e., the best matching node). The weight vector of this node as well as of the neighboring nodes will then be pulled towards the input vector. The closer the node to the best matching node, the “stronger” it will be pulled. Finally when all data records have been presented several times, the nodes represent the data distribution. In each learning cycle of Viscovery, iterations due to all data records are cumulated and applied at once (“Batch-SOM”). Moreover the number of nodes grows from cycle to cycle from an initially small size to the final size (i.e. number of nodes).
