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
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
Comparative Analysis of Selected Association Rules Types
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
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Discovering association rules among items in large databases is a recognized database mining problem. In the paper, we address the issue of association rules generation in the context of changing user requirements. The data mining system user is frequently interested in results of mining around rules that consists in examining how change of attribute values or addition of new attributes influences the discovered dependencies. The set of association rules is often huge. However it is possible to represent it with usually much smaller set of representative rules. If needed, the user can derive all association rules from the set of representative rules syntactically by means of a cover operator. Incremental solutions of mining around representative rules are discussed in the paper as well.