Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Logic programming and databases
Logic programming and databases
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining generalised disjunctive association rules
Proceedings of the tenth international conference on Information and knowledge management
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
An introduction to symbolic data analysis and the SODAS software
Intelligent Data Analysis
SemGrAM: integrating semantic graphs into association rule mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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Traditional pattern discovery approaches permit to identify frequent patterns expressed in form of conjunctions of items and represent their frequent co-occurrences. Although such approaches have been proved to be effective in descriptive knowledge discovery tasks, they can miss interesting combinations of items which do not necessarily occur together. To avoid this limitation, we propose a method for discovering interesting patterns that consider disjunctions of items that, otherwise, would be pruned in the search. The method works in the relational data mining setting and conserves anti-monotonicity properties that permit to prune the search. Disjunctions are obtained by joining relations which can simultaneously or alternatively occur, namely relations deemed similar in the applicative domain. Experiments and comparisons prove the viability of the proposed approach.