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Given a set of patterns, i.e., a class of expressions about databases, and a predicate to evaluate whether a database satisfies a pattern, the frequent pattern mining task is to determine which patterns are satisfied by a given database. Formally, assume that \(\mathcal{P}\) is a set and q is a predicate \(p : \mathcal{P}\times \{\mathbf{r}\vert \mathbf{r}\ \text{is a database}\} \rightarrow \{\text{true, false}\}\). Elements of \(\mathcal{P}\) are called patterns, and q is a selection criterion over \(\mathcal{P}\). Given a pattern φ in \(\mathcal{P}\) and a database r, φ is selected if q(φ, r ) is true. Given a database r, the theory \(\mathcal{T} /\mathcal{P},\mathbf{r},q)\) of r with respect to \(\mathcal{P}\) and q is \(\mathcal{T} /\mathcal{P},\mathbf{r},q) =\{\phi \in \mathcal{P}\vert q(\phi, \mathbf{r})\ \text{is true}\}\).
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It is important to note that the interpretation of “q(φ,r) is true” depends on the...
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References
Agrawal R, Ramakrishnan S (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, Santiago, 12–15 Sept 1994, pp 487–499
Recommended Reading
Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Discov 1(3):241–258
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Salmenkivi, M. (2017). Frequent Pattern. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_434
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