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 sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Scalable Algorithms for Association Mining
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A further study in the data partitioning approach for frequent itemsets mining
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Graph mining based on a data partitioning approach
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Estimation of execution time of data-intensive out-of-core processes
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.