Parallel coordinates has shown itself to be a powerful method of exploring and visualizing multidimensional data. However, applying this method to large datasets often introduces clutter, resulting in reduced insight of the data under investigation. We present a new technique that combines the classical parallel coordinates plot with a synthesized dimension that uses topological proximity as an indicator of similarity. We resolve the issue of over-plotting and increase the utility of the widely-used parallel coordinates visualization. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Trutschl, M., Kilgore, P. C. S. R., & Cvek, U. (2013). Self-organization in parallel coordinates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8131 LNCS, pp. 351–358). https://doi.org/10.1007/978-3-642-40728-4_44
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