Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Logical Generalization of Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
On Modeling Data Mining with Granular Computing
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
k-nearest Neighbor Classification on Spatial Data Streams Using P-trees
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
Interactive classification using a granule network
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
Combination and decomposition theories of formal contexts based on same attribute set
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Algebra system of constrained concept lattice and its completeness of knowledge representation
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Classification Method for Learning Morpheme Analysis
Journal of Information Technology Research
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This paper studies the problem of classification by using a concept lattice as a search space of classification rules. The left hand side of a classification rule is composed by a concept, including its extension and its intension, and the right hand side is the class label that the concept implies. Particularly, we show that logical concepts of the given universe are naturally associated with any consistent classification rules generated by any partition-based or covering-based algorithm, and can be characterized as a special set of consistent classification rules. An algorithm is proposed to find a set of the most general consistent concepts.