Rules in incomplete information systems
Information Sciences: an International Journal
On semantic issues connected with incomplete information databases
ACM Transactions on Database Systems (TODS)
Incomplete Information: Rough Set Analysis
Incomplete Information: Rough Set Analysis
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discernibility functions and minimal rules in non-deterministic information systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A Grey-Rough Set Approach for Interval Data Reduction of Attributes
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Modelling of rough-fuzzy classifier
WSEAS TRANSACTIONS on SYSTEMS
Systems modelling on the basis of rough and rough-fuzzy approach
WSEAS Transactions on Information Science and Applications
Lower and Upper Approximations of Rules in Non-deterministic Information Systems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Information system classification
ISTASC'08 Proceedings of the 8th conference on Systems theory and scientific computation
Rules and Apriori Algorithm in Non-deterministic Information Systems
Transactions on Rough Sets IX
On Possible Rules and Apriori Algorithm in Non-deterministic Information Systems: Part 2
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Application of rough sets theory in air quality assessment
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Classification model based on rough and fuzzy sets theory
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
| Hi-index | 0.00 |
A framework of rule generation in Non-deterministicInfor- mationSystems (NISs), which follows rough sets based rule generation in DeterministicInformationSystems (DISs), is presented. We have already coped with certainrules and minimalcertainrules, which are characterized by the concept of consistency, in NISs. We also introduced discernibilityfunctions into NISs. In this paper, possiblerules in NISs are focused on. Because of the information incompleteness, huge number of possiblerules may exist, and we introduce Min-Maxstrategy and Max-Maxstrategy into possible rule generation in NISs. Possible rules based on these strategies are characterized by the criteria minimumsupport, maximumsupport, minimumaccuracy and maximumaccuracy, and Apriori based algorithm is applied.