Visual Data Mining of Symbolic Knowledge using Rough Sets, Virtual Reality and Fuzzy Techniques: An Application in Geophysical Prospecting

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TypeArticle
ConferenceProceedings of the International Conference on Conceptual Structures 2007 (ICCS 2007), May 27-30, 2007.
AbstractVisual data mining using nonlinear virtual reality spaces (VR) is applied to symbolic knowledge in the form of production rules obtained by rough sets methods in a classification problem with partially defined and imprecise classes. In the context of a geophysical prospecting problem aiming at finding underground caves, a virtual reality nonlinear space for production rules is constructed.The distribution of the rough sets derived rules is characterized by a fuzzy model in both the original 5D space and in the 3D VR space. The membership function of the target class (the presence of a cave) is transferred from the rules to the data objects covered by the corresponding rules and mapped back to the original physical space. The fuzzy model built in the VR space predicted sites where new caves could be expected and one of them was confirmed.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49300
NPARC number8913394
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Record identifier667ce664-8211-47ed-9ee2-1906d32b7622
Record created2009-04-22
Record modified2016-05-09
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