Integration of the Self-Organizing Map and Neural gas with Multidimensional Scaling

In the paper, two combinations (consecutive and integrated) of vector quantization methods (self-organizing map and neural gas) and multidimensional scaling (MDS) have been investigated and compared. The vector quantization is used to reduce the number of dataset items. The dataset with a smaller number of items is analyzed by multidimensional scaling in order to reduce the number of features of data (dimensionality of space) and to map them onto the plane, i.e., to visualize. Some ways of the initialization (at random, on a line, by PCs and by variances) of twodimensional vectors in MDS have been investigated. Two ways of assignment of two-dimensional vectors in the integrated combinations of MDS and vector quantization methods have been examined, too.