Definition
Given a set \(\mathcal{D}\) of examples, a language \(\mathcal{L}\) possible patterns, and a minimum frequency min_ fr , every pattern \(\theta \in \mathcal{L}\) that occurs at least in the minimum number of examples, i.e., | { \(e \in \mathcal{D}\vert \theta\) occurs in e} | ≥ min_ fr , is a frequent pattern. Discovery of all frequent patterns is a common data mining task. In its most typical form, the patterns are frequent itemsets. A more general formulation of the problem is constraint-based mining.
Motivation and Background
Frequent patterns can be used to characterize a given set of examples: they are the most typical feature combinations in the data.
Frequent patterns are often used as components in larger data mining or machine learning tasks. In particular, discovery of frequent itemsets was actually first introduced as an intermediate step in association rule mining (Agrawal et al. 1993) (“frequent itemsets” were then called “large”). The frequency and confidence...
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Toivonen, H. (2017). Frequent Pattern. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_318
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