Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Constraint-Based Mining

  • Siegfried Nijssen
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_164


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:
  • A database \(\mathcal{D}\)

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Recommended Reading

  1. 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.Google Scholar
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  9. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Siegfried Nijssen

There are no affiliations available