Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine 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
Applications of inductive logic programming
Communications of the ACM
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning - special issue on inductive logic programming
Fast discovery of association rules
Advances in knowledge discovery and data mining
Logical settings for concept-learning
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
On Multi-class Problems and Discretization in Inductive Logic Programming
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Detecting Traffic Problems with ILP
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Exploiting relationships for object consolidation
Proceedings of the 2nd international workshop on Information quality in information systems
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Domain-independent data cleaning via analysis of entity-relationship graph
ACM Transactions on Database Systems (TODS)
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
CSM-SD: Methodology for contrast set mining through subgroup discovery
Journal of Biomedical Informatics
Relational association mining based on structural analysis of saturation clauses
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Local patterns: theory and practice of constraint-based relational subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
An approach to learning relational probabilistic FO-PCL knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
On the problem of reversing relational inductive knowledge representation
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Three companion systems, CLAUDIEN, ICL and TILDE, are presented. They use a common representation for examples and hypotheses: each example is represented by a relational database. This contrasts with the classical inductive logic programming systems such as PROGOL and FOIL. It is argued that this representation is closer to attribute value learning and hence more natural. Furthermore, the three systems can be considered first order upgrades of typical data mining systems, which induce association rules, classification rules or decision trees respectively.