A New Approach to Cluster Visualization Methods Based on Self-Organizing Maps

Abstract

The Self-Organizing Map (SOM) is one of the artificial neural networks that perform vector quantization and vector projection simultaneously. Due to this characteristic, a SOM can be visualized twice: through the out-put space, which means considering the vector projection perspective, and through the input data space, em-phasizing the vector quantization process. This paper aims at the idea of presenting high-dimensional clusters that are ‘disjoint objects’ as groups of pairwise disjoint simple geometrical objects – like 3D-spheres for in-stance. We expand current cluster visualization methods to gain better overview and insight into the existing clusters. We analyze the classical SOM model, insisting on the topographic product as a measure of degree of topology preservation and treat that measure as a judge tool for admissible neural net dimension in dimension reduction process. To achieve better performance and more precise results we use the SOM batch algorithm with toroidal topology. Finally, a software solution of the approach for mobile devices like iPad is presented