Spatial data methods and vague regions: A rough set approach

Uncertainty management has been considered essential for real world applications, and spatial data and geographic information systems in particular require some means for managing uncertainty and vagueness. Rough sets have been shown to be an effective tool for data mining and uncertainty management...

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Bibliographic Details
Published in:Applied soft computing 2007-01, Vol.7 (1), p.425-440
Main Authors: Beaubouef, Theresa, Petry, Frederick E., Ladner, Roy
Format: Article
Language:English
Subjects:
Rough sets
Spatial data
Uncertainty
Vague regions
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Summary:Uncertainty management has been considered essential for real world applications, and spatial data and geographic information systems in particular require some means for managing uncertainty and vagueness. Rough sets have been shown to be an effective tool for data mining and uncertainty management in databases. The 9-intersection, region connection calculus (RCC) and egg–yolk methods have proven useful for modeling topological relations in spatial data. In this paper, we apply rough set definitions for topological relationships based on the 9-intersection, RCC and egg–yolk models for objects with broad boundaries. We show that rough sets can be used to express and improve on topological relationships and concepts defined with these models.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2004.11.003