  Parallel coordinates plotting is one of the most popular methods for
multivariate visualization. However, when applied to larger data sets, there
tends to be a "black screen problem," with the screen becoming so cluttered and
full that patterns are difficult or impossible to discern. Xie and Matloff
(2014) proposed remedying this problem by plotting only the most
frequently-appearing patterns, with frequency defined in terms of
nonparametrically estimated multivariate density. This approach displays
"typical" patterns, which may reveal important insights for the data. However,
this remedy does not cover variables that are discrete or categorical. An
alternate method, still frequency-based, is presented here for such cases. We
discretize all continuous variables, retaining the discrete/categorical ones,
and plot the patterns having the highest counts in the dataset. In addition, we
propose some novel approaches to handling missing values in parallel
coordinates settings.
