Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On Modeling Data Mining with Granular Computing
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Modelling Medical Diagnostic Rules Based on Rough Sets
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Granular computing and dual Galois connection
Information Sciences: an International Journal
Relationship between generalized rough sets based on binary relation and covering
Information Sciences: an International Journal
Financial time-series analysis with rough sets
Applied Soft Computing
A granular computing framework for self-organizing maps
Neurocomputing
Evaluation Method for Decision Rule Sets
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Granulations Based on Semantics of Rough Logical Formulas and Its Reasoning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Granular computing based on a generalized approximation space
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A model PM for preprocessing and data mining proper process
Transactions on rough sets VI
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Extended random sets for knowledge discovery in information systems
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Two-phase rule induction from incomplete data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Transactions on computational science II
Transactions on rough sets XII
On classification with missing data using rough-neuro-fuzzy systems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
Theoretical study of granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Granular logic with closeness relation "∼λ" and its reasoning
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A classification model: syntax and semantics for classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Evolutionary computation and rough set-based hybrid approach to rule generation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Rule measures tradeoff using game-theoretic rough sets
BI'12 Proceedings of the 2012 international conference on Brain Informatics
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A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family of granules defined by values of an attribute, in each step, we concentrate on the selection of a single granule. This leads to finding a covering of the universe, which is more general than partition based methods. For the design of granule selection heuristics, several measures on granules are suggested.