Synonyms
Definition
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...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, pp 307–328
Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Discov 5(3):213–246
Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-99), pp 43–52
Fürnkranz J, Gamberger D, Lavrač N (2012) Foundations of rule learning. Springer, Heidelberg
Herrera F, Carmona C, Gonzlez P, del Jesus M (2011) An overview on subgroup discovery: foundations and applications. Knowl Info Syst 29(3):495–525
Klösgen W, May M (2002) Spatial subgroup mining integrated in an object-relational spatial database. In: Proceedings of the 6th European conference on principles and practice of knowledge discovery in databases (PKDD-02), pp 275–286
Kralj Novak P, Lavrač N, Webb GI (2009) Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J Mach Learn Res 10:377–403. http://www.jmlr.org/papers/volume10/kralj-novak09a/kralj-novak09a.pdf
Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of the 4th international conference on knowledge discovery and data mining (KDD-98), pp 80–86
Ross Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81–106
Vavpetič A, Lavrač N (2013) Semantic subgroup discovery systems and workflows in the SDM-toolkit. Comput J 56(3):304–320
Webb GI (1995) OPUS: an efficient admissible algorithm for unordered search. J Artif Intell Res 3:431–465
Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Proceedings of the 1st European conference on principles of data mining and knowledge discovery (PKDD-97), pp 78–87
Wrobel S (2001) Inductive logic programming for knowledge discovery in databases. In: Džeroski S, Lavrač N (eds) Relational data mining, chap 4. Springer, Berlin/New York, pp 74–101
Železný F, Lavrač N (2006) Propositionalization-based relational subgroup discovery with RSD. Mach Learn 62:33–63
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Novak, P.K., Lavrač, N., Webb, G.I. (2017). Supervised Descriptive Rule Induction. 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_808
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_808
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering