Encyclopedia of Machine Learning

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

Supervised Descriptive Rule Induction

  • Petra Kralj Novak
  • Nada Lavrač
  • Geoffrey I. Webb
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_802




Supervised descriptive rule induction (SDRI) is a machine learning task in which individual patterns in the form of rules (see  Classification rule) intended for interpretation are induced from data, labeled by a predefined property of interest. In contrast to standard  supervised rule induction, which aims at learning a set of rules defining a classification/prediction model, the goal of SDRI is to induce individual descriptive patterns. In this respect SDRI is similar to  association rule discovery, but the consequents of the rules are restricted to a single variable – the property of interest – and, except for the discrete target attribute, the data is not necessarily assumed to be discrete.

Supervised descriptive rule induction assumes a set of training examples, described by attributes and their values and a selected attribute of interest (called the target attribute). Supervised descriptive rule induction induces rules that may each be interpreted...

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Recommended Reading

  1. Agrawal, R., Mannila, H., Srikant, R., Toivonon, H., & Inkeri Verkamo, A. (1996). Fast discovery of association rules. In Advances in knowledge discovery and data mining (pp. 307–328). Menlo Park: American Association for Artificial Intelligence.Google Scholar
  2. Bay, S. D., & Pazzani, M. J. (2001). Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 5(3), 213–246.zbMATHCrossRefGoogle Scholar
  3. Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-99) (pp. 43–52). New York: ACM.CrossRefGoogle Scholar
  4. Jovanovski, V., & Lavrač, N. (2001). Classification rule learning with APRIORI-C. In Proceedings of the tenth Portuguese conference on artificial intelligence (pp. 44–51). London: Springer.Google Scholar
  5. Klösgen, W., & May, M. (2002). Spatial subgroup mining integrated in an object-relational spatial database. In Proceedings of the sixth European conference on principles and practice of knowledge discovery in databases (PKDD-02) (pp. 275–286). London: Springer.Google Scholar
  6. Kralj Novak, P. Lavrač, N., & Webb, G. I. (February 2009). Super-vised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of MachineLearning Research, 10, 377–403. Available at: http://www.jmlr.org/papers/volume10/kralj-novak09a/kraljnovak09a.pdf.
  7. Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98) (pp. 80–86).Google Scholar
  8. Trajkovski, I., Lavrac, N., & Tolar, J. (2008). SEGS: Search for enriched gene sets in microarray data. Journal of Biomedical Informatics, 41(4), 588–601.CrossRefGoogle Scholar
  9. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.Google Scholar
  10. Webb, G. I. (1995). OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3, 431–465.zbMATHGoogle Scholar
  11. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In Proceedings of the first European conference on principles of data mining and knowledge discovery (PKDD-97) (pp. 78–87). London: Springer.Google Scholar
  12. Wrobel, S. (2001). Inductive logic programming for knowledge discovery in databases. In S. Dzeroski & N. Lavrac (Eds.), Relational datamining (Chap. 4, pp. 74–101). Berlin: Springer.Google Scholar
  13. Železný, F., & Lavrac, N. (2006). Propositionalization-based relational subgroup discovery with RSD. Machine Learning, 62, 33–63.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Petra Kralj Novak
  • Nada Lavrač
  • Geoffrey I. Webb

There are no affiliations available