GRG: knowledge discovery using information generalization, information reduction, and rule generation

We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, the reduction technique is applied to generate a minimized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database