The importance of rough approximation for information retrieval
International Journal of Man-Machine Studies
The EGG/YOLK reliability hierarchy: semantic data integration using sorts with prototypes
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Handling Spatial Uncertainty in Binary Images: A Rough Set Based Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Topological solution of missing attribute values problem in incomplete information tables
Information Sciences: an International Journal
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Uncertainty management is necessary for real world applications, especially spatial data and geographic information systems. The egg-yolk method has proven useful for representing vague regions in spatial data. Rough sets have been shown to be an effective tool for data mining and uncertainty management in databases. In this initial work, we apply rough set definitions for topological relationships previously defined for the egg-yolk method for continuous space. We show that rough sets can be used to express and improve on topological relationships and concepts defined with the egg-yolk model, and extend it to work for discrete space through the use of rough set indiscernibility.