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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-56372

Mazeika, Arturas; Böhlen, Michael Hanspeter; Mylov, Peer (2008). Using Nested Surfaces for Visual Detection of Structures in Databases. In: Simoff, Simeon J.; Böhlen, Michael Hanspeter; Mazeika, Arturas. Visual Data Mining: Theory, Techniques and Tools for Visual Analytics. Berlin / Heidelberg, 91-102. ISBN 978-3-540-71079-0.

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We define, compute, and evaluate nested surfaces for the purpose of visual data mining. Nested surfaces enclose the data at various density levels, and make it possible to equalize the more and less pronounced structures in the data. This facilitates the detection of multiple structures, which is important for data mining where the less obvious relationships are often the most interesting ones. The experimental results illustrate that surfaces are fairly robust with respect to the number of observations, easy to perceive, and intuitive to interpret. We give a topology-based definition of nested surfaces and establish a relationship to the density of the data. Several algorithms are given that compute surface grids and surface contours, respectively.

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
DDC:000 Computer science, knowledge & systems
Deposited On:04 Jun 2012 11:17
Last Modified:28 Nov 2012 12:20
Series Name:Lecture Notes in Computer Science
Number: 4404/2008
Publisher DOI:10.1007/978-3-540-71080-6_7
Other Identification Number:merlin-id:2320
Citations:Google Scholar™

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