Two-step Modified SOM for Parallel Calculation

This paper presents a simple modification of classic Kohonen network (SOM), which allows parallel processing of input data vectors or partitioning the problem in case of insufficient memory for all vectors from the training set. The algorithm pre-selects potential centroids of data clusters and uses them as weight vectors in the final SOM network. We have demonstrated the usage of this algorithm on images as well as on two well-known datasets representing handwritten digits.