• DocumentCode
    1559982
  • Title

    Information visualization and visual data mining

  • Author

    Keim, Daniel A.

  • Author_Institution
    AT&T Shannon Res. Labs., Florham Park, NJ, USA
  • Volume
    8
  • Issue
    1
  • fYear
    2002
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section
  • Keywords
    data mining; data visualisation; data type; distortion technique; information visualization; interaction technique; visual data exploration; visual data mining; Data mining; Data visualization; Floods; Hardware; Helium; History; Humans; Machine learning; Statistics; Visual databases;
  • fLanguage
    English
  • Journal_Title
    Visualization and Computer Graphics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1077-2626
  • Type

    jour

  • DOI
    10.1109/2945.981847
  • Filename
    981847