C4.5: programs for machine learning
C4.5: programs for machine learning
Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Generalization-based data mining in object-oriented databases using an object cube model
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Dynamic memory revisited
Machine Learning
Knowledge discovery from data?
IEEE Intelligent Systems
Knowledge Management: Problems, Promises, Realities, and Challenges
IEEE Intelligent Systems
InfoFrax: CBR in Fused Cast Refractory Manufacture
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
From Troubleshooting to Process Design: Closing the Manufacturing Loop
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Decision tree induction with CBR
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Process changes in manufacturing are often done by trial and error, even when experienced domain personnel are involved. This is mainly due to the fact that in many domains the number of parameters involved is large and there exists only a partial understanding of interrelationships between them. This paper describes a framework for keeping track of process change experiments, before they qualify as full cases. Process changes happen as a result of diagnosis done by the expert, following which some therapy is decided. The paper also presents an algorithm for diagnosis and therapy based on induction on discrimination trees constructed on specific views on the set of problem parameters.