Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Foundations of cognitive science
Foundations of cognitive science
Foundations of cognitive science
Encouraging Experimental Results on Learning CNF
Machine Learning
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
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
Machine Learning
Granular Computing: An Emerging Paradigm
Granular Computing: An Emerging Paradigm
On Modeling Data Mining with Granular Computing
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Mining Optimal Actions for Profitable CRM
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Rough Set Theory and Granular Computing
Rough Set Theory and Granular Computing
On Intelligence
Concept Formation and Learning: A Cognitive Informatics Perspective
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Toward nature-inspired computing
Communications of the ACM
A multiview approach for intelligent data analysis based on data operators
Information Sciences: an International Journal
A Ten-year Review of Granular Computing
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
The Theoretical Framework and Cognitive Process of Learning
COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics
Software Engineering Foundations: A Software Science Perspective
Software Engineering Foundations: A Software Science Perspective
A layered reference model of the brain (LRMB)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
An empirical comparison of rule sets induced by LERS and probabilistic rough classification
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
Set-theoretic models of granular structures
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Partitions, coverings, reducts and rule learning in rough set theory
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Set-theoretic Approaches to Granular Computing
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Granular modelling of signals: A framework of Granular Computing
Information Sciences: an International Journal
In Search of Effective Granulization with DTRS for Ternary Classification
International Journal of Cognitive Informatics and Natural Intelligence
Measure method of fuzzy inclusion relation in granular computing
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Information Sciences: an International Journal
Granular Computing Based on Gaussian Cloud Transformation
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Cognitive informatics and granular computing are two emerging fields of study concerning information and knowledge processing. A central notion to this processing is information and knowledge granularity. Concepts, as the basic units of thought underlying human intelligence and communication, may play a fundamental role when integrating the results from the two fields in terms of information and knowledge coding, representation, communication, and processing. While cognitive informatics focuses on information processing in the abstract, in machines, and in the brain, granular computing models such processing at multiple levels of granularity. In this paper, we examine a conceptual framework for concept learning from the viewpoints of cognitive informatics and granular computing. Within the framework, we interpret concept learning based on a layered model of knowledge discovery.