Parallel coordinates

. Parallel coordinates plots are a common method of visualizing high-dimensional datasets to analyze multivariate data having multiple variables, or attributes. To plot, or visualize, a set of points in n-dimensional space, n parallel lines are drawn over the background representing coordinate axes, typically oriented vertically with equal spacing. Points in n-dimensional space are represented as individual polylines with n vertices placed on the parallel axes corresponding to each coordinate entry of the n-dimensional point, vertices are connected with n-1 polyline segments. This data visualization is similar to time series visualization, except that Parallel Coordinates are applied to data which do not correspond with chronological time. Therefore, different axes arrangements can be of interest, including reflecting axes horizontally, or inverting the attribute range.

.]] Parallel coordinates plots are a common method of visualizing high-dimensional datasets to analyze multivariate data having multiple variables, or attributes. To plot, or visualize, a set of points in n-dimensional space, n parallel lines are drawn over the background representing coordinate axes, typically oriented vertically with equal spacing. Points in n-dimensional space are represented as individual polylines with n vertices placed on the parallel axes corresponding to each coordinate entry of the n-dimensional point, vertices are connected with n-1 polyline segments. This data visualization is similar to time series visualization, except that Parallel Coordinates are applied to data which do not correspond with chronological time. Therefore, different axes arrangements can be of interest, including reflecting axes horizontally, or inverting the attribute range. ==Higher dimensions== On the plane with an XY Cartesian coordinate system, adding more dimensions in parallel coordinates (often abbreviated ||-coords, PCP, or PC) involves adding more axes. The value of parallel coordinates is that certain geometrical properties in high dimensions transform into easily seen 2D patterns. For example, a set of points on a line in n-space transforms to a set of polylines in parallel coordinates all intersecting at n − 1 points. For n = 2 this yields a point-line duality pointing out why the mathematical foundations of parallel coordinates are developed in the projective rather than Euclidean space. A pair of lines intersects at a unique point which has two coordinates and, therefore, can correspond to a unique line which is also specified by two parameters (or two points). In contrast, more than two points are required to specify a curve and also a pair of curves may not have a unique intersection. Hence by using curves in parallel coordinates instead of lines, the point line duality is lost together with all the other properties of projective geometry, and the known nice higher-dimensional patterns corresponding to (hyper)planes, curves, several smooth (hyper)surfaces, proximities, convexity and recently non-orientability. The goal is to map n-dimensional relations into 2D patterns. Hence, parallel coordinates is not a point-to-point mapping but rather a nD subset to 2D subset mapping, there is no loss of information. Note: even a point in nD is not mapped into a point in 2D, but to a polygonal line—a subset of 2D. ==Statistical considerations== When used for statistical data visualisation there are three important considerations: the order, the rotation, and the scaling of the axes. The order of the axes is critical for finding features, and in typical data analysis many reorderings will need to be tried. Some authors have come up with ordering heuristics which may create illuminating orderings. The rotation of the axes is a translation in the parallel coordinates and if the lines intersected outside the parallel axes it can be translated between them by rotations. The simplest example of this is rotating the axis by 180 degrees. Scaling is necessary because the plot is based on interpolation (linear combination) of consecutive pairs of variables. Therefore, the variables must be in common scale, and there are many scaling methods to be considered as part of data preparation process that can reveal more informative views. A smooth parallel coordinate plot is achieved with splines. In the smooth plot, every observation is mapped into a parametric line (or curve), which is smooth, continuous on the axes, and orthogonal to each parallel axis. This design emphasizes the quantization level for each data attribute. ==Further reading== Heinrich, Julian and Weiskopf, Daniel (2013) State of the Art of Parallel Coordinates, Eurographics 2013 - State of the Art Reports, pp. 95–116 Moustafa, Rida (2011) Parallel coordinate and parallel coordinate density plots, Wiley Interdisciplinary Reviews: Computational Statistics, Vol 3(2), pp. 134–148. Weidele, Daniel Karl I. (2019) Conditional Parallel Coordinates, IEEE Visualization Conference (VIS) 2019, pp. 221–225 ==External links== Alfred Inselberg's Homepage, with Visual Tutorial, History, Selected Publications and Applications An Investigation of Methods for Visualising Highly Multivariate Datasets by C. Brunsdon, A. S. Fotheringham & M. E. Charlton, University of Newcastle, UK Using Curves to Enhance Parallel Coordinate Visualisations by Martin Graham & Jessie Kennedy, Napier University, Edinburgh, UK Parallel Coordinates, a tutorial by Robert Kosara Conditional Parallel Coordinates – Recursive variant of Parallel Coordinates, where a categorical value can expand to reveal another level of Parallel Coordinates.

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