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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
A Tight Upper Bound on the Number of Candidate Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent pattern discovery with memory constraint
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Estimating the number of frequent itemsets in a large database
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Capturing truthiness: mining truth tables in binary datasets
Proceedings of the 2009 ACM symposium on Applied Computing
Issues in pattern mining and their resolutions
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Approximating the number of frequent sets in dense data
Knowledge and Information Systems
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
Performance study of distributed Apriori-like frequent itemsets mining
Knowledge and Information Systems
Efficient prime-based method for interactive mining of frequent patterns
Expert Systems with Applications: An International Journal
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In the context of mining for frequent patterns using the standard levelwise algorithm, the following question arises: given the current level and the current set of frequent patterns, what is the maximal number of candidate patterns that can be generated on the next level? We answer this question by providing tight upper bounds, derived from a combinatorial result from the sixties by Kruskal and Katona. Our result is useful to secure existing algorithms from a combinatorial explosion of the number of candidate patterns.