Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Elements of relational database theory
Handbook of theoretical computer science (vol. B)
Machine learning: an integrated framework and its applications
Machine learning: an integrated framework and its applications
The Utility of Knowledge in Inductive Learning
Machine Learning
Inductive logic programming and learnability
ACM SIGART Bulletin
Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
On the Structure of Armstrong Relations for Functional Dependencies
Journal of the ACM (JACM)
Knowledge Acquisition and Machine Learning
Knowledge Acquisition and Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Predicate Invention in Inductive Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Prediction Rule Discovery Based on Dynamic Bias Selection
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Detecting Interesting Instances
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Tailoring Representations to Different Requirements
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Proceedings of the 2005 conference on Multi-Relational Data Mining
Compact representation of knowledge bases in ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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When learning from very large databases, the reduction of complexityis extremely important. Two extremes of making knowledge discoveryin databases (KDD) feasible have been put forward. One extreme is tochoose a very simple hypothesis language, thereby being capable ofvery fast learning on real-world databases. The opposite extreme isto select a small data set, thereby being able to learn veryexpressive (first-order logic) hypotheses. A multistrategy approachallows one to include most of these advantages and exclude most ofthe disadvantages. Simpler learning algorithms detect hierarchieswhich are used to structure the hypothesis space for a more complexlearning algorithm. The better structured the hypothesis space is,the better learning can prune away uninteresting or losing hypothesesand the faster it becomes.We have combined inductive logic programming (ILP) directly with arelational database management system. The ILP algorithm iscontrolled in a model-driven way by the user and in a data-driven wayby structures that are induced by three simple learning algorithms.