Fast discovery of association rules
Advances in knowledge discovery and data mining
Discovery of relational association rules
Relational Data Mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th 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
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Prospects and challenges for multi-relational data mining
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Inductive inference of VL decision rules
ACM SIGART Bulletin
PRL: A probabilistic relational language
Machine Learning
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
A New ILP-based Concept Discovery Method for Business Intelligence
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Data mining in deductive databases using query flocks
Expert Systems with Applications: An International Journal
A probabilistic interpretation of precision, recall and F-score, with implication for evaluation
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
BET: an inductive logic programming workbench
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Hybrid learning system for adaptive complex event processing
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Genetic algorithm-based optimized association rule mining for multi-relational data
Intelligent Data Analysis
| Hi-index | 12.05 |
Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discovery method, namely Confidence-based Concept Discovery (C^2D), is described in which strong declarative biases and user-defined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. We also describe how to define and use aggregate predicates as background knowledge in the proposed method. In order to use aggregate predicates, we show how to handle numerical attributes by using comparison operators on them. Finally, we analyze the effect of incorporating unrelated facts for generating transitive rules on the proposed method. A set of experiments are conducted on real-world problems to test the performance of the proposed method.