Rough-set inspired approach to knowledge discovery in business databases

We present an approach to knowledge discovery in databases that is based on the idea of the attribute space partition. An inspiration for this approach was the methodology of the rough set theory. Two systems, ProbRough and TRANCE, which are representative of this approach are capable of inducing decision rules from databases with practically unlimited number of objects. The beam search strategy of ProbRough is guided by the global cost criterion and leads to inducing rough classifiers which are mainly intended for making decisions concerning new unseen objects. In case of TRANCE, the exhaustive search strategy in a space of user pre-specified models is guided by the criterion expressed in terms of local properties of the model. The relationships between values of attributes and decisions, detected by both systems, are presented in the form of rules that are easily understood by humans. The presented approach to knowledge discovery is illustrated on two real-world examples from database marketing. The rules induced by ProbRough and TRANCE provided a lot of useful information on customer behavior patterns and about the phenomenon of customer retention.