Definition
Constraint-based mining is the research area studying the development of data mining algorithms that search through a pattern or model space restricted by constraints. The term is usually used to refer to algorithms that search for patterns only. The most well-known instance of constraint-based mining is the mining of frequent patterns. Constraints are needed in pattern mining algorithms to increase the efficiency of the search and to reduce the number of patterns that are presented to the user, thus making knowledge discovery more effective and useful.
Motivation and Background
Constraint-based pattern mining is a generalization of frequent itemset mining. For an introduction to frequent itemset mining, see Frequent Patterns.A constraint-based mining problem is specified by providing the following elements:
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A database \(\mathcal{D}\), usually consisting of independent transactions (or instances)
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A hypothesis space \(\mathcal{L}\) of patterns
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A constraint \(q(\theta...
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Recommended Reading
Bayardo, R. J., Jr., Agrawal, R., & Gunopulos, D. (1999). Constraint-based rule mining in large, dense databases. In Proceedings of the 15th international conference on data engineering (ICDE) (pp. 188–197). Sydney, Australia.
Bucila, C., Gehrke, J., Kifer, D., & White, W. M. (2003). DualMiner: A dual-pruning algorithm for itemsets with constraints. Data Mining and Knowledge Discovery, 7(3), 241–272.
De Raedt, L., Jaeger, M., Lee, S. D., & Mannila, H. (2002). A theory of inductive query answering (extended abstract). In Proceedings of the second IEEE international conference on data mining (ICDM) (pp. 123–130). Los Alamitos, CA: IEEE Press.
Imielinski, T., & Mannila, H. (1996). A database perspective on knowledge discovery. Communications of the ACM, 39, 58–64.
Kifer, D., Gehrke, J., Bucila, C., & White, W. M. (2003). How to quickly find a witness. In Proceedings of the twenty-second ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (pp. 272–283). San Diego, CA: ACM Press.
Mannila, H., & Toivonen, H. (1997). Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3), 241–258.
Morishita, S., & Sese, J. (2000). Traversing itemset lattices with statistical metric pruning. In Proceedings of the nineteenth ACM SIGACT-SIGMOD-SIGART symposium on database systems (PODS) (pp. 226–236). San Diego, CA: ACM Press.
Pei, J., & Han, J. (2002). Constrained frequent pattern mining: A pattern-growth view. SIGKDD Explorations, 4(1), 31–39.
Zhu, F., Yan, X., Han, J., & Yu, P. S. (2007). gPrune: A constraint pushing framework for graph pattern mining. In Proceedings of the sixth Pacific-Asia conference on knowledge discovery and data mining (PAKDD). Lecture notes in computer science (Vol. 4426, pp. 388–400). Berlin: Springer.
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Nijssen, S. (2011). Constraint-Based Mining. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_164
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DOI: https://doi.org/10.1007/978-0-387-30164-8_164
Publisher Name: Springer, Boston, MA
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