Editing Parallel coordinates (section)

==Statistical considerations==
[[File:Parallel coordinates-sample.png|thumb|Representative sample for parallel coordinates.]]
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.<ref>{{cite journal |first1=Jing |last1=Yang |first2=Wei |last2=Peng |first3=Matthew O. |last3=Ward |first4=Elke A. |last4=Rundensteiner |year=2003 |url=http://davis.wpi.edu/~xmdv/docs/tr0313_osf.pdf |title=Interactive Hierarchical Dimension Ordering Spacing and Filtering for Exploration of High Dimensional Datasets |pages=3–4 |journal=IEEE Symposium on Information Visualization (INFOVIS 2003) }}</ref>

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.<ref name="Gpc2" />

Scaling is necessary because the plot is based on interpolation (linear combination) of consecutive pairs of variables.<ref name="Gpc2">{{cite book |first1=Rida |last1=Moustafa |first2=Edward J. |last2=Wegman |chapter=Multivariate continuous data – Parallel Coordinates |editor1= Unwin, A. |editor2=Theus, M. |editor3=Hofmann, H. |title=Graphics of Large Datasets: Visualizing a Million |publisher=Springer |pages=143–156 |year=2006 |isbn=978-0387329062 }}</ref> 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.<ref name="Gpc1">{{cite journal |first1=Rida |last1=Moustafa |first2=Edward J. |last2=Wegman |title=On Some Generalizations of Parallel Coordinate Plots |journal=Seeing a Million, A Data Visualization Workshop, Rain Am Lech (Nr.), Germany |year=2002 |url=http://herakles.zcu.cz/seminars/docs/infovis/papers/Moustafa_generalized_parallel_coordinates.pdf |archive-url=https://web.archive.org/web/20131224111246/http://herakles.zcu.cz/seminars/docs/infovis/papers/Moustafa_generalized_parallel_coordinates.pdf |url-status=dead |archive-date=2013-12-24 }}</ref> 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.<ref name="Gpc2" />