Abstract
When there are both numerical and nominal attributes in a database, existing data mining systems (such as rule induction and decision tree construction) discretize numerical domains into intervals and the discretized intervals are treated in a similar way to nominal values during induction. This paper describes a type of fuzzy intervals implemented in the HCV version 2.0 rule induction software for the interpretation of rule induction results when rules with sharp intervals do not clearly apply to a test example at hand. A battery of experimental results with HCV show that these fuzzy intervals are useful.
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