Generating rules and reasoning under inconsistencies

As the amount of information in the world steadily increases, there is a growing demand for tools for analyzing this information. In this paper, we investigate the problem of data mining, i.e. constructing decision rules from a set of primitive input data. The main contention is that there is a need to be able to generate decision rules and to reason in presence of inconsistencies. Propositional default rules are generated in this paper. Based on Skowron´s default rule generation method (see T. Mollestad & A. Skowron, Proc. 9th Internat. Symposium on Foundations of Intell. Syst., pp. 448-457, 1996) and our analysis of inconsistencies, we develop a method for default rule generation from a decision table and its corresponding reasoning method. Any as-yet-unseen object can be processed with the rules generated by our rule-generating method and reasoning method