Parallel coordinate and parallel coordinate density plots



The parallel coordinate plot (PCP)—which represents a p-dimensional data point in Cartesian coordinates by a polyline (or curve) intercepting p-parallel axes—is a viable tool for hyperdimensional data visualization. It enables the human visual system to spot informative patterns in complex data and gain better understanding of the underlying geometry of hyperdimensional objects. Correlated records, conceptual clusters, and outliers are easy to discern with the PCP. The parallel coordinate density plot integrates the PCP with density estimation techniques to visualize concentrated information instead of the profiles themselves. Thus mitigating the visual cluttering burden inherent in the plot for a few thousand records. In this article, we give an overview of the PCP, their generalizations, the use of orthogonal bases to smooth out the system, and density estimation techniques to overcome the visual cluttering limitations inherent in the plot. We discuss the duality theorem and its usability in identifying patterns visually or by automatic means. We discuss the effect of scaling the data and the profiles. We provide some visualization examples on different datasets. WIREs Comp Stat 2011 3 134–148 DOI: 10.1002/wics.145

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