Induction of decision rules in classification and discovery-oriented perspectives

This paper discusses induction of decision rules from data tables represent ing information about a set of objects described by a set of attributes. If the input data contains inconsistencies, rough sets theory can be used to handle them. The most popular perspectives of rule induction are classifica tion and knowledge discovery. The evaluation of decision rules is quite dif ferent depending on the perspective. Criteria for evaluating the quality of a set of rules are presented and discussed. The degree of conflict and the possibility of achieving a satisfying compromise between criteria relevant to classification and criteria relevant to discovery are then analyzed. Fo r this purpose, we performed an extensive experimental study on several wel l-known data sets where: we compared two different approaches: (1) the popu lar rough set based rule induction algorithm LEM2 generating classification rules, (2) our own algorithm Explore-specific for discovery perspective. ( C) 2001 John Wiley & Sons, Inc.