ICS: An Interactive Classification System
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
KT: Knowledge Technology -- The Next Step of Information Technology (IT)
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
An extensive study on automated Dewey Decimal Classification
Journal of the American Society for Information Science and Technology
Domain-oriented data-driven data mining (3DM): simulation of human knowledge understanding
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Introduction to 3DM: domain-oriented data-driven data mining
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Classification based on logical concept analysis
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
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Classification is one of the main tasks in machine learning, data mining and pattern recognition. Compared with the extensively studied data-driven approaches, the interactively user-driven approaches are less explored. A granular computing model is suggested for re-examining the classification problems. An interactive classification method using the granule network is proposed, which allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. This method is complementary to the existing classification methods.