Scientific data visualization using three-dimensional self-organizing feature maps

The goal of scientific data visualization is to transform numeric or symbolic data into simple coherent patterns for enhanced human interpretation. It involves a combination of exploratory data analysis and data visualization techniques that create a new level of information providing a deeper look at the underlying structures present in high dimensional data. This paper discusses how a spherical self-organizing feature map (SOFM) enables multivariate numeric data to take a geometric form by mapping high dimensional data to a 3D, space, thereby providing a mechanism to explore large numeric databases for coherent patterns. The patterns present in the numeric data are given a shape based on similarity. The performance of the proposed visualization algorithm is tested using coordinate data from known geometry and multi-spectral satellite data