Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data

A dataset with M items has 2M subsets, any one of which may be the one we really want. With a good data display, our own fantastic pattern-recognition abilities can not only sort through this combinatorial explosion, but they can also extract insights fromthe visual patterns. These are the core reasons for data visualization. With parallel coordinates (abbrev. f-coords), the search for multivariate relations in highdimensional datasets is transformed into a 2-D pattern recognition problem. In this chapter, the guidelines and strategy for knowledge discovery using parallel coordinates are illustrated on various real datasets, one with 400 variables froma manufacturing process. A geometric classification algorithm based on f-coords is presented and applied to complex datasets. It has low computational complexity, providing the classification rule explicitly and visually.Theminimal set of variables required to state the rule are found and ordered by their predictive value. A visual economic model of a real country is constructed and analyzed to illustrate how multivariate relations can be modeled using hypersurfaces.The overview at the end provides a basic summary of f-coords and a prelude of what is on the way: the distillation of relational information into patterns that eliminate need for polygonal lines altogether.