Title :
Fuzzy interpretation of discretized intervals
Author_Institution :
Dept. of Math. & Comput. Sci., Colorado Sch. of Mines, Golden, CO
fDate :
12/1/1999 12:00:00 AM
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
Keywords :
data mining; decision trees; fuzzy logic; fuzzy set theory; software packages; HCV version 2.0; data mining; decision trees; discretized intervals; fuzzy interpretation; fuzzy intervals; fuzzy logic; fuzzy set theory; rule induction software; Batteries; Data mining; Databases; Decision trees; Logic; Machine learning; Machine learning algorithms; Pattern recognition; Software testing; Statistical analysis;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
