A method for computing all maximally general rules in attribute-value systems

A method for finding all deterministic and maximally general rules for a target classification is explained in detail and illustrated with examples: Maximally general rules are rules with minimal numbers of conditions. The method has been developed within the context of the rough sets model and is based on the concepts of a decision matrix and a decision function. The problem of finding ail the rules is reduced to the problem of computing prime implicants of a group of associated Boolean expressions. The method is particularly applicable to identifying all potentially interesting deterministic rules in a knowledge discovery system but can also be used to produce possible rules or nondeterministic rules with decision probabilities, by adapting the method to the definitions of the variable precision rough sets model.