Adjustable general line coordinates for visual knowledge discovery in n-D data (no. 12)
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-9-2017
Abstract
Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.
Recommended Citation
Kovalerchuk, B., & Grishin, V. (2019). Adjustable general line coordinates for visual knowledge discovery in n-D data. Information Visualization, 18(1), 3–32. https://doi.org/10.1177/1473871617715860
Journal
Information Visualization
Rights
© The Author(s) 2017
Comments
This article was originally published in Information Visualization. The full-text article from the publisher can be found here. Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.