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    Pairwise display of high dimensional information via Eulerian tours and Hamiltonian decompositions


    Hurley, Catherine B. and Oldfield, R.W. (2010) Pairwise display of high dimensional information via Eulerian tours and Hamiltonian decompositions. Journal of Computational and Graphical Statistics, 19 (4). pp. 861-886. ISSN 1061-8600

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    Abstract

    A graph theoretic approach is taken to the component order problem in the layout of statistical graphics. Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics. Similar traversals of edge weighted graphs are used to amplify the visual effect of selected salient features in the data. Relevant graph theory is summarized and classic algorithms are tailored to this problem. Graphics for multiple comparisons are reviewed and a new display developed that is based on graph traversal. Improved star glyph displays of multivariate data are described. Parallel coordinate displays tailored to particular features of the data are developed. The methods and new graphical displays are made available as an R package, PairViz.

    Item Type: Article
    Additional Information: Preprint version of published article, which is available at DOI: 10.1198/jcgs.2010.09136 . C.B. Hurley's research supported in part by a Research Frontiers Grant from Science Foundation Ireland. R.W. Oldfield's research supported in part by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.
    Keywords: Complete graph; Cycle decompositions; Graph decomposition; Graph traversal algorithms; Graphics layout; GrEul; Mean–mean plots; Multiple comparisons; PairViz R package; Parallel coordinates; Scagnostics; Seriation; Star glyphs; Visual clustering; Weighted graph; WHam;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 5552
    Identification Number: https://doi.org/10.1198/jcgs.2010.09136
    Depositing User: Dr. Catherine Hurley
    Date Deposited: 17 Nov 2014 15:30
    Journal or Publication Title: Journal of Computational and Graphical Statistics
    Publisher: American Statistical Association
    Refereed: Yes
    Funders: Science Foundation Ireland, Natural Sciences and Engineering Research Council of Canada
    URI:

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