Knowledge discovery in general, and data mining in particular, have received a growing interest both from research and industry in recent years. Its main aim is to look for previously unknown relationships or patterns representing knowledge hidden in real-life data sets . The typical representations of knowledge discovered from data are: associations, trees or rules, relational logic clauses, functions, clusters or taxonomies, or characteristic descriptions of concepts [16, 29, 21]. In this paper we focus on the rule-based representation. More precisely, we are interested in
that are considered in
problems. In data mining other types of rules are also considered, e.g., association rules or action rules [16, 29, 34], however, in the text hereafter we will use the general term “rules” to refer specifically to decision rules.