Fast discovery of association rules
Advances in knowledge discovery and data mining
Relational Data Mining
Database Management Systems
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Using Prior Probabilities and Density Estimation for Relational Classification
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for defining the hypothesis search space through Inverse Entailment using multiple example seeds.