Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Fast Parallel Association Rule Mining without Candidacy Generation
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
Optimization of frequent itemset mining on multiple-core processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Parallel FP-growth on PC cluster
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Although the FP-Growth association-rule mining algorithm is more efficient than the Apriori algorithm, it has two disadvantages. The first is that the FP-tree can become too large to be created in memory; the second is the serial processing approach used. In this paper, a kind of parallel association-rule mining algorithm has been proposed. It does not need to create an overall FP-tree, and it can distribute data mining tasks over several computing nodes to achieve parallel processing. This approach will greatly improve efficiency and processing ability when used for mining association rules and is suitable for association-rule mining on massive data sets.