Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Approximation Space and LEM2-like Algorithms for Computing Local Coverings
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
Dependence-space-based attribute reduction in consistent decision tables
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
The concept of reducts in pawlak three-step rough set analysis
Transactions on Rough Sets XVI
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When applying rough set theory to rule learning, one commonly associates equivalence relations or partitions to a complete information table and tolerance relations or coverings to an incomplete table. Such associations are sometimes misleading.We argue that Pawlak threestep approach for data analysis indeed uses both partitions and coverings for a complete information table. A slightly different formulation of Pawlak approach is given based on the notions of attribute reducts of a classification table, attribute reducts of objects and rule reducts. Variations of Pawlak approach are examined.