QGPCP: Quantized Generalized Parallel Coordinate Plots for Large Multivariate Data Visualization

Abstract

This article proposes a new hybrid visualization technique that integrates a frequency-based model and a generalized parallel coordinate plot (GPCP), thus mitigating the visual cluttering of GPCP. In the new technique, a GPCP’s profile lines (or curves) with similar frequencies are detected and saturated with appropriate color intensity corresponding to the frequencies. The technique may be employed to enhance a family of visualization tools—the Andrews plot and scatterplot matrix, for example. In addition to the new technique’s efficiency in reducing visual clutter in the multivariate data visualization techniques, it is computationally feasible, easy to implement, and has important mathematical and statistical properties. The reliability and accuracy of the technique are demonstrated through extensive experiments on challenging datasets, both simulated and real. These datasets are high in dimensions and large so that they cannot be explored with GPCP or frequency-based techniques alone.The datasets for pollen, OUT5D, and California housing are available in the online supplements.