Abstract
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
Keywords
Association rule learningDatabase transactionComputer sciencePruningDatabaseData miningApriori algorithmTransaction dataData warehouseAssociation (psychology)Affinity analysisTransaction processingInformation retrieval
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Publication Info
- Year
- 1993
- Type
- article
- Pages
- 207-216
- Citations
- 14674
- Access
- Closed
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Cite This
Rakesh Agrawal,
Tomasz Imieliński,
Arun Swami
(1993).
Mining association rules between sets of items in large databases.
, 207-216.
https://doi.org/10.1145/170035.170072
Identifiers
- DOI
- 10.1145/170035.170072