The Rough Sets approached is based on a mathematical concept given by Pawlak [Paw91]. We describe Rough Sets in more detail in the next chapter, and just sketch its most important features here. Using concepts of certain and possible membership to a class, rules for classification may be created in various ways.
The Rough Sets methodology provides definitions and methods for finding which attributes separates one class or classification from another. Since inconsistencies are allowed and membership in a set does not have to be absolute, the potential for handling noise gracefully is big. The results from a training phase when using the Rough Sets approach will usually be a set of propositional rules which may be said to have syntactic and semantic simplicity for a human. How the problems of dynamic databases and time and memory constraints are dealt with will be different for each system using the Rough Sets approach, but typically the time complexity will be high and perhaps non-polynomial.