Inverting entailment and Progol
Machine intelligence 14
Machine Learning - special issue on inductive logic programming
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Principles of knowledge representation
A sequential sampling algorithm for a general class of utility criteria
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Logical Definitions from Relations
Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
On Multi-class Problems and Discretization in Inductive Logic Programming
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Comparison of ILP and Propositional Systems on Propositional Traffic Data
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
ACM SIGKDD Explorations Newsletter
Multi-relational Data Mining: a perspective
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Finding Association Rules That Trade Support Optimally against Confidence
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Transformation-Based Learning Using Multirelational Aggregation
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Dirichlet enhanced relational learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Finding association rules that trade support optimally against confidence
Intelligent Data Analysis
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
Transductive Learning from Relational Data
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Mining relational association rules for propositional classification
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Local patterns: theory and practice of constraint-based relational subgroup discovery
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Dimensionality reduction in data summarization approach to learning relational data
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Relational data mining algorithms and systems are capable of directly dealing with multiple tables or relations as they are found in today's relational databases. This reduces the need for manual preprocessing and allows problems to be treated that cannot be handled easily with standard single-table methods. This paper provides a tutorial-style introduction to the topic, beginning with a detailed explanation of why and where one might be interested in relational analysis. We then present the basics of Inductive Logic Programming (ILP), the scientific field where relational methods are primarily studied. After illustrating the working of MIDOS, a relational methods for subgroup discovery, in more detail, we show how to use relational methods in one of the current data mining systems.