Definition
Rule-based methods are a popular class of techniques in machine learning and data mining (Fürnkranz et al. 2012). They share the goal of finding regularities in data that can be expressed in the form of an IF-THEN rule. Depending on the type of rule that should be found, we can discriminate between association rule discovery and predictive rule learning. In the latter case, one is often also interested in learning a collection of rules that collectively cover the instance space in the sense that they can make a prediction for every possible instance.
Characteristics
Association Rule Discovery
The discovery of association rules typically happens in two phases, which were pioneered in the Apriori algorithm. First, all frequent itemsets (i.e., conditions that cover a certain minimum number of examples) are found. In a second pass, these are then converted into association rules.
For finding all frequent itemsets, Apriorigenerates all rules with a certain minimum frequency in...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bringmann B, Nijssen S, Zimmermann A (2009) Pattern-based classification: a unifying perspective. In: Knobbe A, Fürnkranz J (eds) From local patterns to global models: proceedings of the ECML/PKDD-09 workshop (LeGo-09), Bled, Slovenia, pp 36–50
Cohen WW (1995) Fast effective rule induction. In: Prieditis A, Russell S (eds) Proceedings of the 12th international conference on machine learning (ML-95), Lake Tahoe, CA. Morgan Kaufmann, San Mateo, pp 115–123
Fürnkranz J (1997) Pruning algorithms for rule learning. Machine Learning 27(2):139–171
Fürnkranz J (1999) Separate-and-conquer rule learning. Artif Intell Rev 13(1):3–54
Fürnkranz J, Gamberger D, Lavrač N (2012) Fundamentals of rule learning. Springer, Berlin
Gamberger D, Lavrac N, Zelezný F, Tolar J (2004) Induction of comprehensible models for gene expression datasets by subgroup discovery methodology. J Biomed Inform 37(4):269–284
Goethals B (2005) Frequent set mining. In: Maimon O, Rokach L (eds) The data mining and knowledge discovery handbook. Springer-Verlag, New York, pp 377–397
Kralj Novak P, Lavrač N, Webb GI (2009) Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J Machine Learn Res 10:377–403
Muggleton S (1999) Scientific knowledge discovery using inductive logic programming. Commun ACM 42(11):42–46
Parthasarathy S, Tatikonda S, Ucar D (2010) A survey of graph mining techniques for biological datasets. In: Aggarwal CC, Wang H (eds) Managing and mining graph data, vol 40, Advances in database systems. Springer-Verlag, New York, pp 547–580
Wang JT-L, Zaki MJ, Toivonen H, Shasha D (eds) (2005) Data mining in bioinformatics. Springer, London. ISBN 1-85233-671-4
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques with java implementations, 2nd edn. Morgan Kaufmann, San Francisco
Zimmermann A, De Raedt L (2009) Cluster-grouping: from subgroup discovery to clustering. Machine Learning 77(1):125–159
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this entry
Cite this entry
Fürnkranz, J. (2013). Rule-based Methods. In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_610
Download citation
DOI: https://doi.org/10.1007/978-1-4419-9863-7_610
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9862-0
Online ISBN: 978-1-4419-9863-7
eBook Packages: Biomedical and Life SciencesReference Module Biomedical and Life Sciences