Abstract
Association rules can be extracted from datasets, where each example consists of a set of items. An association rule has the form \(X \rightarrow Y\), where X and Y are itemsets, and the interpretation is that if set X occurs in an example, then set Y is also likely to occur in the example. Association rules were originally motivated by supermarket basket analysis, but as a domain-independent technique, they have found applications in numerous fields. Association rule mining is part of the larger field of frequent itemset or frequent pattern mining.
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References
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington, DC. ACM, New York, pp 207–216
Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, pp 307–328
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Toivonen, H. (2023). Association Rule. In: Phung, D., Webb, G.I., Sammut, C. (eds) Encyclopedia of Machine Learning and Data Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7502-7_13-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_13-1
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