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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: Springer, 91-102.

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Abstract

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.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2008
Deposited On:04 Jun 2012 11:17
Last Modified:10 Jun 2015 14:01
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number: 4404/2008
ISBN:978-3-540-71079-0
Publisher DOI:10.1007/978-3-540-71080-6_7
Other Identification Number:merlin-id:2320

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