title: Fuzzy versus quantitative association rules: a fair data-driven comparison.
abstract: As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.
authors.forename: Hannes
authors.initials: H
authors.lastname: Verlinde
authors.forename: Martine
authors.initials: M
authors.lastname: De Cock
authors.forename: Raymond
authors.initials: R
authors.lastname: Boute
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical