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 discovery of association rules
Advances in knowledge discovery and data mining
Efficiently Determining the Starting Sample Size for Progressive Sampling
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Evaluation of Sampling for Data Mining of Association Rules
Evaluation of Sampling for Data Mining of Association Rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Efficiently mining frequent itemsets is the key step in extracting association rules from large scale databases. Considering the restriction of min_support in mining association rules, a weighted sampling algorithm for mining frequent itemsets is proposed in the paper. First of all, a weight is given to each transaction data. Then according to the statistical optimal sample size of database, a sample is extracted based on weight of data. In terms of the algorithm, the sample includes large amounts of transaction data consisting of the frequent itemsets with many items inside, so that the frequent itemsets mined from sample are similar to those gained from the original data. Furthermore, the algorithm can shrink the sample size and guarantee the sample quality at the same time. The experiment verifys the validity