Concept lattices defined from implication operators
Fuzzy Sets and Systems
Journal of Systems and Software - Special issue on artificial and computational intelligence for decisions, control, and automation in engineering and industrial applications
Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Modal-style operators in qualitative data analysis
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A multi-level conceptual data reduction approach based on the Lukasiewicz implication
Information Sciences: an International Journal - Special issue: Information technology
Reduction method for concept lattices based on rough set theory and its application
Computers & Mathematics with Applications
Granular Computing and Knowledge Reduction in Formal Contexts
IEEE Transactions on Knowledge and Data Engineering
Attribute reduction in fuzzy concept lattices based on the T implication
Knowledge-Based Systems
Rule acquisition and attribute reduction in real decision formal contexts
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Formal query systems on contexts and a representation of algebraic lattices
Information Sciences: an International Journal
Multigranulation rough sets: From partition to covering
Information Sciences: an International Journal
Core set analysis in inconsistent decision tables
Information Sciences: an International Journal
Quick attribute reduction in inconsistent decision tables
Information Sciences: an International Journal
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Rule acquisition is one of the main purposes in the analysis of real decision formal contexts. In general, the decision rules derived directly from a real decision formal context are not concise or compact. In order to derive more compact decision rules, this study proposes a rule acquisition oriented framework of knowledge reduction for real decision formal contexts and formulates a corresponding reduction method by constructing a discernibility matrix and its associated Boolean function. The proposed reduction method is applicable to any real decision formal contexts and with the reduced real decision formal contexts, we can obtain more compact decision rules that can imply all the decision rules derived from the initial real decision formal context. Some numerical experiments are conducted to assess the efficiency of the proposed method.