Vagueness in spatial data: Rough set and egg-yolk approaches

Abstract

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.