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
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Interpreting Low and High Order Rules: A Granular Computing Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Approximation Space and LEM2-like Algorithms for Computing Local Coverings
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
Information Sciences: an International Journal
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
Partitions, coverings, reducts and rule learning in rough set theory
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Association reducts: a framework for mining multi-attribute dependencies
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Ensembles of bireducts: towards robust classification and simple representation
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Knowledge Reduction in Random Incomplete Decision Tables via Evidence Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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Rough set approaches to data analysis involve removing redundant attributes, redundant attribute-value pairs, and redundant rules in order to obtain a minimal set of simple and general rules. Pawlak arranges these tasks into a three-step sequential process based on a central notion of reducts. However, reducts used in different steps are defined and formulated differently. Such an inconsistency in formulation may unnecessarily affect the elegancy of the approach. Therefore, this paper introduces a generic definition of reducts of a set, uniformly defines various reducts used in rough set analysis, and examines several mathematically equivalent, but differently formulated, definitions of reducts. Each definition captures a different aspect of a reduct and their integration provides new insights.