Top-Frequency Parallel Coordinates Plots
This work addresses visualization challenges for analysts dealing with multivariate data that includes discrete or categorical variables, offering an incremental improvement over existing methods.
The paper tackles the 'black screen problem' in parallel coordinates plots for large datasets by proposing a frequency-based method that discretizes continuous variables and plots the most common patterns, also introducing novel approaches for handling missing values.
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.