Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining Knowledge Rules from Databases: A Rough Set Approach
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Structures in Logic and Computer Science, A Selection of Essays in Honor of Andrzej Ehrenfeucht
A Rough Set Framework for Data Mining of Propositional Default Rules
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Induction of Classification Rules from Imperfect Data
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Rule Discovery from Databases with Decision Matrices
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Binary relation based rough sets
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
The measures relationships study of three soft rules based on granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Using VPRS to mine the significance of risk factors in IT project management
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
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As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. The problem of data mining is investigated in this paper. It is very important and useful to generate decision rules and reason under inconsistency. Propositional default rules are generated in this paper. Based on analysis of inconsistency, Skowron's default rule generation algorithm is improved. A corresponding reasoning method with a rule-choosing stratagem of lower frequency first under inconsistency is also developed. A suitable decision can be generated for any yet unseen object including one with unknown attribute values and one that is even inconsistent (conflicting) with objects of the training decision table. The rule-choosing stratagem is shown to be valid by our experiments.