Hyperdimensional Data Analysis Using Parallel Coordinates

TLDR

Several alternatives to parallel coordinates for high‑dimensional data analysis are reviewed. The study presents basic results of employing parallel coordinate representation as a high‑dimensional data analysis tool. The authors describe the parallel‑coordinate algorithm, its projective‑transformation properties, duality results, axis permutations, and extensions. The paper reports extensions of the parallel‑coordinate idea and discusses implementation experiences.

Abstract

Abstract This article presents the basic results of using the parallel coordinate representation as a high-dimensional data analysis tool. Several alternatives are reviewed. The basic algorithm for parallel coordinates is laid out and a discussion of its properties as a projective transformation is given. Several duality results are discussed along with their interpretations as data analysis tools. Permutations of the parallel coordinate axes are discussed, and some examples are given. Some extensions of the parallel coordinate idea are given. The article closes with a discussion of implementation and some of my experiences.