Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Inducing Theory for the Rule Set
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
On Certain Rough Inclusion Functions
Transactions on Rough Sets IX
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
Rough validity, confidence, and coverage of rules in approximation spaces
Transactions on Rough Sets III
Interpolation Models for Spatiotemporal Association Mining
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Rough Sets and Association Rule Generation
Fundamenta Informaticae
Rule quality measure-based induction of unordered sets of regression rules
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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. The number of association rules may be huge. To alleviate this problem, we introduced in [1] a notion of representative association rules. Representative association rules are a least set of rules that covers all association rules satisfying certain user specified constraints. The association rules, which are not representative ones, may be generated by means of a cover operator without accessing a database. In this paper, we investigate properties of representative association rules and offer a new efficient algorithm computing such rules.