Generating rules and reasoning under inconsistencies

作者：Guoyin Wang, Yu Wu, Feixu Liu · 年份：2002 · DOI：10.1109/iecon.2000.972397 · 被引用次数：15 · 研究领域：Rough Sets and Fuzzy Logic、Data Mining Algorithms and Applications、Data Management and Algorithms

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.