Parallel Coordinates: Visualization and Data Mining for High Dimensionsal Data

Alfred Inselberg
Tel Aviv University

The desire to understand the underlying geometry of multidimensional problems motivated several visualization methodologies to augment our limited 3-dimensional perception. After a short overview, Parallel Coordinates are rigorously developed obtaining a 1-1 mapping between subsets of Euclidean N-space and subsets of 2-space. It leads to representations of lines, flats, curves, intersections, hypersurfaces, proximities and geometrical construction algorithms. Convexity can be visualized in ANY dimension as well as non-orientability (Moebius strip) and other properties of hypersurfaces. The development is interlaced with several applications including Visual and Automatic Data Mining on real high dimensional datasets.

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