Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining generalised disjunctive association rules
Proceedings of the tenth international conference on Information and knowledge management
Applied Intelligence
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Association Mining in Large Databases: A Re-examination of Its Measures
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Semantic Analytical Reports: A Framework for Post-processing Data Mining Results
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
The GUHA method and its meaning for data mining
Journal of Computer and System Sciences
Using disjunctions in association mining
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Background knowledge and PMML: first considerations
Proceedings of the 2011 workshop on Predictive markup language modeling
SEWEBAR-CMS: semantic analytical report authoring for data mining results
Journal of Intelligent Information Systems
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This paper proposes the GUHA AR Model, an XML Schema-based formalism for representing the setting and results of association rule (AR) mining tasks. In contrast to the item-based representation of the PMML 4.0 Association Model, the proposed expresses the association rule as a couple of general boolean attributes related by condition on one or more arbitrary interest measures. This makes the GUHA AR Model suitable also for other than apriori-based AR mining algorithms, such as those mining for disjunctive or negative ARs. In addition, there are practically important research results on special logical calculi formulas which correspond to such association rules. The GUHA AR Model is intended as a replacement of the PMML AssociationModel. It is tightly linked to the Background Knowledge Exchange Format (BKEF), an XML schema proposed for representation of data-mining related domain knowledge, and to the AR Data Mining Ontology ARON.