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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Ankerst M. Visual Data Mining. Faculty of Mathematics and Computer Science, University of Munich, Munich, 2000.
Chen C. Information Visualization: Beyond the Horizon. Springer, London, 2004.
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.
Demšar U.K. Investigating visual exploration of geospatial data: an exploratory usability experiment for visual data mining. Comput. Environ. Urban., 31:551–571, 2007.
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.
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.
Keim D.A., Mansmann F., Schneidewind J., and Ziegler H. Challenges in visual data analysis. In Proc. Int. Conf. on Information Visualization, 2006.
Keim D.A. and North S.C. Visual data mining in large geospatial point sets. IEEE Comput. Graph., 24(5):36–44, 2004.
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.
B. and Kovalerchuk J. (eds.). Schwing Visual and Spatial Analysis: Advances in Data Mining, Reasoning, and Problem Solving. Springer, Dordrecht, 2004.
Niggemann O. Visual Data Mining of Graph-Based Data. Department of Mathematics and Computer Science, University of Paderborn, Paderborn, Germany, 2001.
Shneiderman B. Inventing discovery tools: combining information visualization with data mining, In Proc. Discovery Science, 2001, pp. 17–28.
S.J., Simoff M., and Böhlen A. (eds.). Mazeika Visual Data Mining: Theory, Techniques and Tools for Visual Analytics. Springer, Heidelberg, 2008.
Soukup T. and Davidson I. Visual Data Mining: Techniques and Tools for Data Visualization and Mining. John Wiley & Sons, London, 2002.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
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
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_1121
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering