Visualization techniques of self-organizing maps

Neural networks try, in a computing way, to simulate human brain, including its behavior, by making errors and learning and thereby making new discovers. Self-organizing maps are part of a neural network group based on competitive networks where competition is used as a way of learning. They try to find similarities between data, based only on input data, grouping similar data to each other and thereby forming clusters. Selforganizing maps learning it's unsupervised. It can adapt its behavior without any previous knowledge and also, without human intervention. The maps can make connections between the observations that were made and the expected result. Its result enables improvements in future decisions. The main point of this master thesis is the selforganizing maps speed improvement and a presentation of a possible way to visualize large dimensions maps, once the only way to do it, it's by getting to know if the learning process was achieved.