Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data.
Some major rule induction paradigms are:
- Association rule learning algorithms (e.g., Agrawal)
- Decision rule algorithms (e.g., Quinlan 1987)
- Hypothesis testing algorithms (e.g., RULEX)
- Horn clause induction
- Version spaces
- Rough set rules
- Inductive Logic Programming
- Boolean decomposition (Feldman)
Some rule induction algorithms are:
- Sahami, Mehran. "Learning classification rules using lattices." Machine learning: ECML-95 (1995): 343-346.
- Quinlan, J. R. (1987). "Generating production rules from decision trees" (PDF). In McDermott, John (ed.). Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87). Milan, Italy. pp. 304–307.
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