Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
The Paradigm of Granular Rough Computing: Foundations and Applications
COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics
On the idea of using granular rough mereological structures in classification of data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On classification of data by means of rough mereological granules of objects and rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Rough mereological classifiers obtained from weak variants of rough inclusions
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On Classifying Mappings Induced by Granular Structures
Transactions on Rough Sets IX
Granular computing applied to ontologies
International Journal of Approximate Reasoning
Reasoning about concepts by rough mereological logics
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On classification of data by means of rough mereological granules of objects and rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
A fuzzy view on rough satisfiability
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Transactions on rough sets XII
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
A Logic-Algebraic Approach to Graded Inclusion
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
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This work aims at presenting to a wider audience fundamental notions and ideas of rough mereology. We discuss various methods for constructing rough inclusions in data sets, then we show how to apply them to the task of knowledge granulation, and finally, we introduce granular reflections of data sets with examples of classifiers built on them.