IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i7p3616-3644.html
   My bibliography  Save this article

High-dimensional data visualisation: The textile plot

Author

Listed:
  • Kumasaka, Natsuhiko
  • Shibata, Ritei

Abstract

The textile plot is a parallel coordinate plot in which the ordering, locations and scales of the axes are simultaneously chosen so that the connecting lines, each of which represents a case, are aligned as horizontally as possible. Plots of this type can accommodate numerical data as well as ordered or unordered categorical data, or a mixture of these different data types. Knots and parallel wefts are features of the textile plot which greatly aid the interpretation of the data. Several practical examples are presented which illustrate the potential usefulness of the textile plot as an aid to the interpretation of multivariate data.

Suggested Citation

  • Kumasaka, Natsuhiko & Shibata, Ritei, 2008. "High-dimensional data visualisation: The textile plot," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3616-3644, March.
  • Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3616-3644
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00451-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Theus, Martin, 2002. "Interactive Data Visualization using Mondrian," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i11).
    2. George Michailidis & Jan Leeuw, 2001. "Data Visualization through Graph Drawing," Computational Statistics, Springer, vol. 16(3), pages 435-450, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sei, Tomonari, 2016. "An objective general index for multivariate ordered data," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 247-264.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    2. John W. Emerson, 2008. "Interactive and Dynamic Graphics for Data Analysis: With R and GGobi by COOK, D. and SWAYNE, D," Biometrics, The International Biometric Society, vol. 64(4), pages 1301-1303, December.
    3. Michailidis, George & De Leeuw, Jan, 2005. "Homogeneity analysis using absolute deviations," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 587-603, March.
    4. Tomokazu Fujino, 2007. "SVG+Ajax+R: a new framework for WebGIS," Computational Statistics, Springer, vol. 22(4), pages 511-520, December.
    5. C. Hurley & R. Oldford, 2011. "Eulerian tour algorithms for data visualization and the PairViz package," Computational Statistics, Springer, vol. 26(4), pages 613-633, December.
    6. Zhao, S.L. & Cacciolatti, L. & Lee, S.H. & Song, W., 2015. "Regional collaborations and indigenous innovation capabilities in China: A multivariate method for the analysis of regional innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 202-220.
    7. repec:jss:jstsof:31:i04 is not listed on IDEAS
    8. Simon Urbanek, 2009. "How to talk to strangers: ways to leverage connectivity between R, Java and Objective C," Computational Statistics, Springer, vol. 24(2), pages 303-311, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3616-3644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.