Rough computational methods for information systems
Artificial Intelligence
A Rough Set Framework for Data Mining of Propositional Default Rules
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005, ... / Lecture Notes in Artificial Intelligence)
Flow graphs and decision algorithms
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
The computational complexity of inference using rough set flow graphs
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Transactions on Rough Sets III
Transactions on Rough Sets IV
Inference and Reformation in Flow Graphs Using Granular Computing
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Extended Pawlak's Flow Graphs and Information Theory
Transactions on Computational Science V
Interpretation of extended Pawlak flow graphs using granular computing
Transactions on rough sets VIII
Novel matrix forms of rough set flow graphs with applications to data integration
Computers & Mathematics with Applications
An interpretation of flow graphs by granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Adaptive Method of Adjusting Flowgraph for Route Reconstruction in Video Surveillance Systems
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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In knowledge discovery, Pawlak's flow graph is a new mathematical model and has some distinct advantages. However, the flow graph can not effectively deal with some situations, such as estimating consistence and removing redundant attributes. A primary reason is that it is a quantitative graph and requires the network to be steady. Therefore, we propose an extension of the flow graph which takes objects flowing in network as its basis to study the relations among the information in this paper. It not only has the capabilities of the flow graph, but also can implement some functions as well as decision table