Synonyms
Immersive data mining; VDM; Visual analysis; Visual data analysis; Visual discovery
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. London: Springer; 2004.
Chittaro L, Combi C, Trapasso G. Data mining on temporal data: a visual approach and its clinical application to hemodialysis. J Visual Lang Comput. 2003;14(6):591–620.
Demšar UK. Investigating visual exploration of geospatial data: an exploratory usability experiment for visual data mining. Comput Environ Urban. 2007;31(5):551–71.
de Oliveira F, Crisina M, Levkowitz H. From visual data exploration to visual data mining: a survey. IEEE Trans Vis Comput Gr. 2003;9(3):378–94.
Isenberg P, Tang A, Carpendale S. An exploratory study of visual information analysis. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2008.
Keim DA, Mansmann F, Schneidewind J, Ziegler H. Challenges in visual data analysis. In: Proceedings of the International Conference on Information Visualization; 2006.
Keim DA, North SC. Visual data mining in large geospatial point sets. IEEE Comput Graph. 2004;24(5):36–44.
Keim DA, Sips M, Ankerst M. Visual data-mining techniques. In: Hansen CD, Johnson CR, editors. Visualization handbook. Amsterdam: Johnson Elsevier; 2005. p. 831–43.
Kovalerchuk B, Schwing J, editors. Visual and spatial analysis: advances in data mining, reasoning, and problem solving. Dordrecht: Springer; 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: Proceedings of the Discovery Science; 2001. p. 17–28.
Simoff SJ, Böhlen M, Mazeika A, editors. Visual data mining: theory, techniques and tools for visual analytics. Heidelberg: Springer; 2008.
Soukup T, Davidson I. Visual data mining: techniques and tools for data visualization and mining. London: Wiley; 2002.
Thomas JJ, Cook KA. Illuminating the path: the research and development agenda for visual analytics. Silver Spring: IEEE CS Press; 2005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Simoff, S.J. (2018). Visual Data Mining. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1121
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1121
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering