Discovery of association rules is a prototypical problem in data mining. The current algorithms proposed for data mining of association rules make repeated passes over the database to determine the commonly occurring itemsets (or set of items). For large databases, the I/O overhead in scanning the database can be extremely high. In this paper we show that random sampling of transactions in the database is an effective method for finding association rules. Sampling can speed up the mining process by more than an order of magnitude by reducing I/O costs and drastically shrinking the number of transaction to be considered. We may also be able to makethe sampled database resident in main-memory. Furthermore, we show that sampling can accurately represent the data patterns in the database with high confidence. We experimentally evaluate the effectiveness of sampling on different databases, and study the relationship between the performance, and the accuracy and confidence of the chosen sample. 1
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