New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
C4.5: programs for machine learning
C4.5: programs for machine learning
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Logical settings for concept-learning
Artificial Intelligence
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Algorithmic Program DeBugging
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Distance based approaches to relational learning and clustering
Relational Data Mining
Relational learning and boosting
Relational Data Mining
Relational data mining applications: an overview
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Top-down induction of first-order logical decision trees
Artificial Intelligence
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
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Situated at the intersection of machine learning and logic programming, inductive logic programming (ILP) has been concerned with finding patterns expressed as logic programs. While ILP initially focussed on automated program synthesis from examples, it has recently expanded its scope to cover a whole range of data analysis tasks (classification, regression, clustering, association analysis). ILP algorithms can this be used to find patterns in relational data, i.e., for relational data mining (RDM). This paper briefly introduces the basic concepts of ILP and RDM and discusses some recent research trends in these areas.