Probabilistic self-organizing maps for qualitative data

We present a self-organizing map model to study qualitative data (also called categorical data). It is based on a probabilistic framework which does not assume any prespecified distribution of the input data. Stochastic approximation theory is used to develop a learning rule that builds an approximation of a discrete distribution on each unit. This way, the internal structure of the input dataset and the correlations between components are revealed without the need of a distance measure among the input values. Experimental results show the capabilities of the model in visualization and unsupervised learning tasks.