Models of incremental concept formation
Artificial Intelligence
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Real-world applications of Bayesian networks
Communications of the ACM
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
Explanation-based learning to recognize network malfunctions
Information-Knowledge-Systems Management
Prototype selection algorithms for distributed learning
Pattern Recognition
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Many expert systems for diagnosis, prediction, and analysis in complex dynamic scenarios use Bayesian networks for reasoning under uncertainty. These networks often benefit from adaptations to their specific conditions by machine learning on operational data. The knowledge encoded in these adapted networks yields insights as to typical modes of operations, configurations, types of usage, etc. To utilize this knowledge for the revision and refinement of existing and future expert systems, we developed a context-sensitive machine learning process that uses a multitude of Bayesian networks as input for concept discovery. Our algorithms allow the identification of typical network fragments, their relations, and the context in which they are valid. With these results, we are able to substitute parts of existing networks that are not yet optimally adapted to their tasks and initiate a knowledge engineering process aiming at a precise network generation for future expert systems which accounts for previously unknown characteristics.