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Visual Data Mining

  • Reference work entry
Encyclopedia of Database Systems
  • 1078 Accesses

Synonyms

Visual data analysis; Visual analysis; Visual discovery; Immersive data mining; VDM

Definition

Visual data mining (VDM) is the process of interaction and analytical reasoning with one or more visual representations of abstract data. The process may lead to the visual discovery of robust patterns in these data or provide some guidance for the application of other data mining and analytics techniques. It facilitates analysts in obtaining deeper understanding of the underlying structures in a data set. The process relies on the tight interconnectedness of tasks, selection of visual representations, the corresponding set of interactive manipulations, and respective analytical techniques. Discovered patterns form the information and knowledge utilized in decision making.

Historical Background

Visual exploration of large data sets had been used as a complementary technique to data mining in order to obtain additional information about the data set. Since the early 1990s there has...

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Recommended Reading

  1. Ankerst M. Visual Data Mining. Faculty of Mathematics and Computer Science, University of Munich, Munich, 2000.

    Google Scholar 

  2. Chen C. Information Visualization: Beyond the Horizon. Springer, London, 2004.

    Google Scholar 

  3. Chittaro L., Combi C., and Trapasso G. Data mining on temporal data: a visual approach and its clinical application to hemodialysis. J. Visual Lang. Comput., 14:591–620, 2003.

    Article  Google Scholar 

  4. Demšar U.K. Investigating visual exploration of geospatial data: an exploratory usability experiment for visual data mining. Comput. Environ. Urban., 31:551–571, 2007.

    Article  Google Scholar 

  5. de Oliveira F., Crisina M., and Levkowitz H. From visual data exploration to visual data mining: a survey. IEEE T. Vis. Comput. Gr., 9(3):378–394, 2003.

    Article  Google Scholar 

  6. Isenberg P., Tang A., and Carpendale S. An exploratory study of visual information analysis. In Proc. SIGCHI Conf. on Human Facters in Computing Systems, 2008.

    Google Scholar 

  7. Keim D.A., Mansmann F., Schneidewind J., and Ziegler H. Challenges in visual data analysis. In Proc. Int. Conf. on Information Visualization, 2006.

    Google Scholar 

  8. Keim D.A. and North S.C. Visual data mining in large geospatial point sets. IEEE Comput. Graph., 24(5):36–44, 2004.

    Article  Google Scholar 

  9. Keim D.A., Sips M., and Ankerst M. Visual data-mining techniques. In Visualization Handbook, C.D., Hansen C.R. (eds.). Johnson Elsevier, Amsterdam, 2005, pp. 831–843.

    Chapter  Google Scholar 

  10. B. and Kovalerchuk J. (eds.). Schwing Visual and Spatial Analysis: Advances in Data Mining, Reasoning, and Problem Solving. Springer, Dordrecht, 2004.

    MATH  Google Scholar 

  11. Niggemann O. Visual Data Mining of Graph-Based Data. Department of Mathematics and Computer Science, University of Paderborn, Paderborn, Germany, 2001.

    Google Scholar 

  12. Shneiderman B. Inventing discovery tools: combining information visualization with data mining, In Proc. Discovery Science, 2001, pp. 17–28.

    Google Scholar 

  13. S.J., Simoff M., and Böhlen A. (eds.). Mazeika Visual Data Mining: Theory, Techniques and Tools for Visual Analytics. Springer, Heidelberg, 2008.

    Google Scholar 

  14. Soukup T. and Davidson I. Visual Data Mining: Techniques and Tools for Data Visualization and Mining. John Wiley & Sons, London, 2002.

    Google Scholar 

  15. Thomas J.J. and Cook K.A. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE CS Press, Silver Spring, MD, 2005.

    Google Scholar 

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Simoff, S.J. (2009). Visual Data Mining. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1121

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