Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Machine Learning - special issue on inductive logic programming
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Discovery of relational association rules
Relational Data Mining
A Machine Learning Approach to Building Domain-Specific Search Engines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
Time and space efficient discovery of maximal geometric graphs
DS'07 Proceedings of the 10th international conference on Discovery science
On enumerating frequent closed patterns with key in multi-relational data
DS'10 Proceedings of the 13th international conference on Discovery science
Inductive databases and constraint-based data mining
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
Mining closed patterns in relational, graph and network data
Annals of Mathematics and Artificial Intelligence
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We investigate the problem of mining closed sets in multi-relational databases. Previous work introduced different semantics and associated algorithms for mining closed sets in multirelational databases. However, insight into the implications of semantic choices and the relationships among them was still lacking. Our investigation shows that the semantic choices are important because they imply different properties, which in turn affect the range of algorithms that can mine for such sets. Of particular interest is the question whether the seminal LCM algorithm by Uno et al. can be upgraded towards multi-relational problems. LCM is attractive since its run time is linear in the number of closed sets and it does not need to store outputs in order to avoid duplicates. We provide a positive answer to this question for some of the semantic choices, and report on experiments that evaluate the scalability and applicability of the upgraded algorithm on benchmark problems.