Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Mining top-k Hot Melody Structures over online music query streams
Pattern Recognition Letters
Efficient Discovery of Top-K Minimal Jumping Emerging Patterns
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Finding N-Most Prevalent Colocated Event Sets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
Fast extraction of adaptive change point based patterns for problem resolution in enterprise systems
DSOM'06 Proceedings of the 17th IFIP/IEEE international conference on Distributed Systems: operations and management
ExMiner: an efficient algorithm for mining top-k frequent patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
Mining association rules based on seed items and weights
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Mining the k-most interesting frequent patterns sequentially
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Previous methods on mining association rules require users to input a minimum support threshold. However, there can be too many or too few resulting rules if the threshold is set inappropriately. It is difficult for end-users to find the suitable threshold. In this paper, we propose a different setting in which the user does not provide a support threshold, but instead indicates the amount of results that is required.