Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Machine Learning - special issue on inductive logic programming
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Applications and Research Problems of Subgroup Mining
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Discovery of First-Order Regularities in a Relational Database Using Offline Candidate Determination
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
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Experimental investigation of pruning methods for relational pattern discovery
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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The discovery of interesting patterns inrelational databases is an important data mining task.This paper is concerned with the development of a search algorithm forfirst-order hypothesis spaces adopting an important pruningtechnique (termed subset pruning here) from associationrule mining in a first-order setting. The basic search algorithmis extended by so-called requires and excludes constraintsallowing to declare prior knowledge about the data, suchas mutual exclusion or generalization relationships among attributes,so that it can be exploited for furtherstructuring and restricting the search space. Furthermore, it isillustrated how to process taxonomies and numerical attributes inthe search algorithm.Several task settings using different interestingness criteria andsearch modes with corresponding pruning criteria are described.Three settings serve as test beds for evaluation of theproposed approach. The experimental evaluation shows that theimpact of subset pruning is significant,since it reduces the number of hypothesis evaluations in many cases by about50%. The impact of generalization relationships is shown to beless effective in our experimental set-up.