Rule induction

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.

Paradigms

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)

Algorithms

Some rule induction algorithms are:

Charade^[1]
Rulex
Progol
CN2

References

^ 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. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)

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[1] Sahami, Mehran. "Learning classification rules using lattices." Machine learning: ECML-95 (1995): 343-346.

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