Lossless Visual Knowledge Discovery in High Dimensional Data with Elliptic Paired Coordinates

Data with more than two or three dimensions are difficult for humans to conceptualize and facilitate knowledge discovery. Novel Elliptic Paired Coordinates (EPCs) allow for multidimensional data to be represented in 2-D without loss of multidimensional information. In addition, EPC halves the required visual elements in the graph in comparison with parallel and radial coordinates. This research explores the effectiveness of constructing predictive machine learning models interactively using EPC visualizations. For this research EllipseVis, an interactive software system, was developed to process high-dimensional datasets, create corresponding EPC visualizations, and build predictive classification models based on dominance rules. The EllipseVis system allows both interactive and automatic discovery of areas that are located with a high percentage of single-class dominance. The experiments using it on benchmark datasets suggest EPC approach is a promising method for discovering predictive models with high coverage and precision that could be useful in many fields allowing for visually appealing dominance rules to be easily interpreted in the application domains.