Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Towards Efficient Re-mining of Frequent Patterns upon Threshold Changes
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
Expert Systems with Applications: An International Journal
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
An efficient approach for interactive mining of frequent itemsets
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users' changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold changes.