Years and Authors of Summarized Original Work
2004; Uno, Kiyomi, Arimura
Problem Definition
Pattern mining is a fundamental problem in data mining. The problem is to find all the patterns appearing in the given database frequently. For a set E = { 1, …, n} of items, an itemset (also called a pattern) is a subset of E. Let \(\mathcal{D}\) be a given database composed of transactionsR 1, …, R m , R i  ⊆ E. For an itemset P, an occurrence of P is a transaction of \(\mathcal{D}\) such that P ⊆ R, and the occurrence setOcc(P) is the set of occurrences of P. The frequency of P, also called support, is | Occ(P) | and denoted by frq(P). For a given constant σ called minimum support, an itemset P is frequent if frq(P) ≥ σ. For given a database and a minimum support, frequent itemset mining is the problem of enumerating all frequent itemsets in \(\mathcal{D}\).
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Uno, T. (2014). Frequent Pattern Mining. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-3-642-27848-8_722-1
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DOI: https://doi.org/10.1007/978-3-642-27848-8_722-1
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