Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Scalable parallel data mining for association rules
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
Hash based parallel algorithms for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Association Rules: Anti-Skew Algorithms
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
Finding Generalized Path Patterns for Web Log Data Mining
ADBIS-DASFAA '00 Proceedings of the East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications: Current Issues in Databases and Information Systems
On Supporting Interactive Association Rule Mining
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
On the Efficiency of Association-Rule Mining Algorithms
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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We propose a family of large itemset counting algorithms which adapt to the amount of main memory available. By using historical or sampling data, the potential large itemsets (candidates) and the false candidates are identified earlier. Redundant computation is reduced(thus overall CPU time reduced) by counting different sizes of candidates together and the use of a dynamic trie. By counting candidates earlier and counting more candidates in each scan, the algorithms reduce the overall number of scans required.