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Investigation of self-organizing map and its use in data clustering
Self-organizing Map (SOM) is a special kind of unsupervised neural network. SOM consists of regular, usually two-dimensional, neurons. During training, SOM forms an elastic net that folds onto the cloud formed by the input data. Thus SOM can be interpreted as a topology preserving mapping from input space onto the two-dimensional grid of neuron. In mining data SOM has been used as a clustering technique. As there are several important issues concerning with data mining clustering techniques some experiments has been done with the goal of discovering the relation between SOM and the issues. This paper discusses SOM, the experiments and the analytical result of how SOM, in some way has provided good solutions to several of the issues.
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