Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of association rules using closed itemset lattices
Information Systems
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Inferring Knowledge from Frequent Patterns
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
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Many knowledge discovery tasks consist in mining databases. Nevertheless, there are cases in which a user is not allowed to access the database and can deal only with a provided fraction of knowledge. Still, the user hopes to find new interesting relationships. Surprisingly, a small number of patterns can be augmented into new knowledge so considerably that its analysis may become infeasible. In the article, we offer a method of inferring the concise lossless and sound representation of association rules in the form of maximal covering rules from a concise lossless representation of all derivable patterns. The respective algorithm is offered as well.