IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4419-0176-7_15.html
   My bibliography  Save this book chapter

Interactive Visualization of Large High-Dimensional Datasets

In: Data Engineering

Author

Listed:
  • Wei Ding

    (University of Massachusetts Boston)

  • Ping Chen

    (University of Houston Downtown)

Abstract

Nowadays many companies and public organizations use powerful database systems for collecting and managing information. Huge amount of data records are often accumulated within a short period of time. Valuable information is embedded in these data, which could help discover interesting knowledge and significantly assist in decision-making process. However, human beings are not capable of understanding so many data records which often have lots of attributes. The need for automated knowledge extraction is widely recognized, and leads to a rapidly developing market of data analysis and knowledge discovery tools.

Suggested Citation

  • Wei Ding & Ping Chen, 2009. "Interactive Visualization of Large High-Dimensional Datasets," International Series in Operations Research & Management Science, in: Yupo Chan & John Talburt & Terry M. Talley (ed.), Data Engineering, chapter 15, pages 335-351, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-0176-7_15
    DOI: 10.1007/978-1-4419-0176-7_15
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:isochp:978-1-4419-0176-7_15. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.