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Frequent itemsets (Agrawal et al., 1993, 1996) are a form of frequent pattern. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset.
For instance, customers of an on-line bookstore could be considered examples, each represented by the set of books he or she has purchased. A set of books, such as {“Machine Learning,” “The Elements of Statistical Learning,” “Pattern Classification,”} is a frequent itemset if it has been bought by sufficiently many customers. Given a frequency threshold, perhaps only 0.1 or 0.01% for an on-line store, all sets of books that have been bought by at least that many customers are called frequent. Discovery of all frequent itemsets is a typical data mining task. The original use has been as part of association rule discovery. Apriori is a classical algorithm for finding frequent itemsets.
The idea generalizes far beyond...
Recommended Reading
Agrawal, R., Imieliński, 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 (pp. 207–216). New York: ACM.
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp. 307–328). Menlo Park: AAAI Press.
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Toivonen, H. (2011). Frequent Itemset. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_317
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DOI: https://doi.org/10.1007/978-0-387-30164-8_317
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