Constraint query languages (preliminary report)
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ECML '93 Proceedings of the European Conference on Machine Learning
Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Learning Linear Constraints in Inductive Logic Programming
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Efficient Induction of Numerical Constraints
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Induction of Constraint Logic Programs
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Introducing External Functions in Constraint Query Languages
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Generating Numerical Literals During Refinement
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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For several years, Inductive Logic Programming (ILP) has been developed into two main directions: on one hand, the classical symbolic framework of ILP has been extended to deal with numeric values and a few works have emerged, stating that an interesting domain for modeling symbolic and numeric values in ILP was Constraint Logic Programming; on the other hand, applications of ILP in the context of Data Mining have been developed, with the benefit that ILP systems were able to deal with databases composed of several relations. In this paper, we propose a new framework for learning, expressed in terms of Constraint Databases: from the point of view of ILP, it gives a uniform way to deal with symbolic/numeric values and it extends the classical framework by allowing the representation of infinite sets of positive/ negative examples; from the point of view of Data Mining, it can be applied not only to relational databases, but also to spatial databases. A prototype has been implemented and experiments are currently in progress.