Visualization of multi-dimensional data with vector-fusion

Multi-dimensional entities are modeled, displayed and understood with a new algorithm vectorizing data of any dimensionality. This algorithm is called SBP; it is a vectorized generalization of parallel coordinates. Classic geometries of any dimensionality can be demonstrated to facilitate perception and understanding of the shapes generated by this algorithm. SBP images of a 4D line, a circle and 3D and 4D spherical helices are shown. A strategy for synthesizing multi-dimensional models matching multi-dimensional data is presented. Current applications include data mining; modeling data-defined structures of scientific interest such as protein structure and Calabi-Yau figures as multi-dimensional geometric entities; generating vector-fused data signature fingerprints of classic frequency spectra that identify substances; and treating complex targets as multi-dimensional entities for automatic target recognition. SBP vector data signatures apply to all pattern recognition problems.