Abstract
Informally, rule learning denotes all algorithms that learn or discover patterns in data, which are formulated in the form of a rule. These can be predictive (e.g., classification rules) or descriptive rules (e.g., association rules or supervised descriptive rule induction). Consequently, the learning algorithms typically differ in the type of search they use for finding these rules in the search space. Exhaustive search is more common in descriptive rule mining, whereas heuristic search using a variety of quality criteria is more commonly used in predictive rule learning. An overview of the field can be found in Fürnkranz et al. (2012).
Similar content being viewed by others
Recommended Reading
Cestnik B (1990) Estimating probabilities: a crucial task in machine learning. In: Aiello L (ed) Proceedings of the 9th European conference on artificial intelligence (ECAI-90). Pitman, Stockholm, pp 147–150
Clark P, Boswell R (1991) Rule induction with CN2: some recent improvements. In: Proceedings of the 5th European working session on learning (EWSL-91). Springer, Porto, pp 151–163
Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283
Cohen WW, Singer Y (1999) A simple, fast, and effective rule learner. In: Proceedings of the 16th national conference on artificial intelligence (AAAI-99). AAAI/MIT Press, Menlo Park, pp 335–342
Domingos P (1996) Unifying instance-based and rule-based induction. Mach Learn 24:141–168
Fürnkranz J (1997) Pruning algorithms for rule learning. Mach Learn 27(2):139–171. http://www.ke.informatik.tu-darmstadt.de/ juffi/publications/mlj97.pdf
Fürnkranz J, Flach PA (2005) ROC ‘n’ rule learning – towards a better understanding of covering algorithms. Mach Learn 58(1):39–77. doi:10.1007/s10994-005-5011-x. http://www.cs.bris.ac.uk/~flach/papers/furnkranz-flach-mlj.pdf
Fürnkranz J, Widmer G (1994) Incremental reduced error pruning. In: Cohen WW, Hirsh H (eds) Proceedings of the 11th international conference on machine learning (ML-94). Morgan Kaufmann, New Brunswick, pp 70–77. http://www.ke.informatik.tu-darmstadt.de/~juffi/publications/ml-94.ps.gz
Fürnkranz J, Gamberger D, Lavrač N (2012) Foundations of rule learning. Springer. doi:10.1007/978-3-540-75197-7. ISBN 978-3-540-75196-0. http://www.springer.com/978-3-540-75196-0
Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Agrawal R, Stolorz P, Piatetsky-Shapiro G (eds) Proceedings of the 4th international conference on knowledge discovery and data mining (KDD-98), New York, pp 80–86
Michalski RS (1996) On the quasi-minimal solution of the covering problem. In: Proceedings of the 5th international symposium on information processing (FCIP-69), vol A3 (Switching circuits), Bled, pp 125–128
Mitchell TM (1982) Generalization as search. Artif Intell 18(2):203–226
Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
Webb GI (1995) OPUS: an efficient admissible algorithm for unordered search. J Artif Intell Res 5: 431–465
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this entry
Cite this entry
Fürnkranz, J. (2016). Rule Learning. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_744-1
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7502-7_744-1
Received:
Accepted:
Published:
Publisher Name: Springer, Boston, MA
Online ISBN: 978-1-4899-7502-7
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering