Information flow: the logic of distributed systems
Information flow: the logic of distributed systems
Rough set methods for the synthesis and analysis of concurrent processes
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A New Method for Determining of Extensions and Restrictions of Information Systems
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
On testing membership to maximal consistent extensions of information systems
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Greedy Algorithm for Construction of Partial Association Rules
Fundamenta Informaticae
On Construction of Partial Association Rules
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
On Minimal Inhibitory Rules for Almost All k-Valued Information Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Rule learning for classification based on neighborhood covering reduction
Information Sciences: an International Journal
Towards a practical approach to discover internal dependencies in rule-based knowledge bases
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Driver status recognition by neighborhood covering rules
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
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The minimal rules for information systems are often used for inducing data models by methods in which the optimization of models is based on the minimal length principle. We show that almost all information systems from a certain large class of information systems have relatively short minimal rules. However, the number of such rules is not polynomial in the number of attributes and the number of objects. This class consists of all binary information systems with the number of objects polynomial in the number of attributes. Hence, for efficient inducing data models some filtration techniques in rule generation are necessary. In our further study we would like to extend our results for arbitrary information systems.