Fast discovery of association rules
Advances in knowledge discovery and data mining
Learning Logical Definitions from Relations
Machine Learning
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
A New ILP-based Concept Discovery Method for Business Intelligence
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
A comparative study on ILP-based concept discovery systems
Expert Systems with Applications: An International Journal
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Multi-relational concept discovery aims to find the relational rules that best describe the target concept. An important challenge that relational knowledge discovery systems face is intractably large search space and there is a trade-off between pruning the search space for fast discovery and generating high quality rules. Combining ILP approach with conventional association rule mining techniques provides effective pruning mechanisms. Due to the nature of Apriori algorithm, the facts that do not have common attributes with the target concept are discarded. This leads to efficient pruning of search space. However, under certain conditions, it fails to generate transitive rules, which is an important drawback when transitive rules are the only way to describe the target concept. In this work, we analyze the effect of incorporating unrelated facts for generating transitive rules in an hybrid relational concept discovery system, namely C2D, which combines ILP and Apriori.