Title :
Generating rules and reasoning under inconsistencies
Author :
Wang, G.Y. ; Wu, Y. ; Liu, F.
Author_Institution :
Inst. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., China
Abstract :
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
Keywords :
data mining; decision tables; learning (artificial intelligence); nonmonotonic reasoning; rough set theory; uncertainty handling; data mining; decision rules; decision table; default rule generation method; inconsistencies; information analysis; knowledge acquisition; primitive input data; propositional default rules; reasoning method; rough sets; unseen object processing; Artificial intelligence; Computer science; Data analysis; Data mining; Humans; Information analysis; Information systems; Knowledge acquisition; Knowledge based systems; Rough sets;
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
DOI :
10.1109/IECON.2000.972397
