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).
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Fürnkranz, J. (2017). Rule Learning. 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_744
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_744
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