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
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second 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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Using Closed Itemsets for Discovering Representative Association Rules
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Fast Discovery of Representative Association Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
The Closed Keys Base of Frequent Itemsets
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Why to Apply Generalized Disjunction-Free Generators Representation of Frequent Patterns?
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Two measures of objective novelty in association rule mining
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
SARM — succinct association rule mining: an approach to enhance association mining
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Formal and computational properties of the confidence boost of association rules
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Discovering association rules among items in a large database is an important database mining problem. However, the number of association rules may be huge. The problem can be alleviated by applying concise lossless representations of association rules. There were proposed a few such representations in the late ninetieths. Representative association rules are such an example representation. The association rules, which are not representative ones, may be derived syntactically from representative rules by means of a cover operator. In the paper we show how to discover all representative rules using only closed itemsets and their generators.