Alfred Inselberg

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A methodology for <b>visualizing</b> analytic and synthetic geometry in <i>R<sup>N</sup></i> is presented. It is based on a system of <b>parallel coordinates</b> which induces a non-projective mapping between N-Dimensional and 2-Dimensional sets. Hypersurfaces are represented by their planar images which have some geometrical properties analogous to the(More)
By means ofParallel Coordinates planar “graphs” of multivariate relations are obtained. Certain properties of the relationship correspond tothe geometrical properties of its graph. On the plane a point ←→ line duality with several interesting properties is induced. A new duality betweenbounded and unbounded convex sets and hstars (a generalization of(More)
The display of multivariate datasets in parallel coordinates, transforms the search for relations among the variables into a 2-D pattern recognition problem. This is the basis for the application to visual data mining. The knowledge discovery process together with some general guidelines are illustrated on a dataset from the production of a VLSI chip. The(More)
Automation has arrived to Parallel Coordinates. A geometrically motivated classifier is presented and applied, with both training and testing stages, to 3 real datasets. Our results compared to those from 23 other classifiers have the least error. The algorithm is based on parallel coordinates and : has very low computational complexity in the number of(More)
Starting from early successes of visualization, like Dr. J. Snow’s dot map in 1854 showing the connection of cholera to a water pump, visualization has grown to a powerful principal as well as supportive tool for data discovery. The 1992 IEEE Visualization Conference’s Grand Challenge Panel identified fundamental problems for visualization. This panel will(More)