shown at a time. In time series visualization, there exists a natural predecessor and successor; therefore in this special case, there exists a preferred arrangement. However, when the axes do not have a unique order, finding a good axis arrangement requires the use of heuristics and experimentation. In order to explore more complex relationships, axes must be reordered. By arranging the axes in 3-dimensional space (however, still in parallel, like nails in a nail bed), an axis can have more than two neighbors in a circle around the central attribute, and the arrangement problem gets easier (for example by using a minimum spanning tree). A prototype of this visualization is available as extension to the data mining software ELKI. However, the visualization is harder to interpret and interact with than a linear order. Software While there are a large number of papers about parallel coordinates, there are only few notable software publicly available to convert databases into parallel coordinates graphics. Notable software are ELKI, GGobi, Mondrian, Orange and ROOT. Libraries include Protovis.js, D3.js provides basic examples. D3.Parcoords.js (a D3-based library) specifically dedicated to parallel coordinates graphic creation has also been published. The Python data structure and analysis library Pandas implements parallel coordinates plotting, using the plotting library matplotlib. Other visualizations for multivariate data Radar chart – a visualization with coordinate axes arranged radially Andrews plot – the Fourier transform of a parallel coordinates graph References 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. Data visualization Multi-dimensional geometry Statistical charts and diagrams