  It is challenging for humans to enable visual knowledge discovery in data
with more than 2-3 dimensions with a naked eye. This chapter explores the
efficiency of discovering predictive machine learning models interactively
using new Elliptic Paired coordinates (EPC) visualizations. It is shown that
EPC are capable to visualize multidimensional data and support visual machine
learning with preservation of multidimensional information in 2-D. Relative to
parallel and radial coordinates, EPC visualization requires only a half of the
visual elements for each n-D point. An interactive software system EllipseVis,
which is developed in this work, processes high-dimensional datasets, creates
EPC visualizations, and produces predictive classification models by
discovering dominance rules in EPC. By using interactive and automatic
processes it discovers zones in EPC with a high dominance of a single class.
The EPC methodology has been successful in discovering non-linear predictive
models with high coverage and precision in the computational experiments. This
can benefit multiple domains by producing visually appealing dominance rules.
This chapter presents results of successful testing the EPC non-linear
methodology in experiments using real and simulated data, EPC generalized to
the Dynamic Elliptic Paired Coordinates (DEPC), incorporation of the weights of
coordinates to optimize the visual discovery, introduction of an alternative
EPC design and introduction of the concept of incompact machine learning
methodology based on EPC/DEPC.
