# Rule induction

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{{Short description|Area of machine learning}}
thumb|Example of a decision tree
'''Rule induction''' is an area of [machine learning](/source/machine_learning) in which formal rules are extracted from a set of observations.  The rules extracted may represent a full [scientific model](/source/scientific_model) of the data, or merely represent local [patterns](/source/patterns) in the data.

[Data mining](/source/Data_mining) in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures.<ref name="TriantaphyllouFelici2006">{{cite book|author1=Evangelos Triantaphyllou|author2=Giovanni Felici|title=Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques|url=https://books.google.com/books?id=AYJGAAAAQBAJ&pg=PA415|date=10 September 2006|publisher=Springer Science & Business Media|isbn=978-0-387-34296-2}}</ref>{{rp|415-}} In the easiest case, a rule is expressed with “if-then statements” and was created with the [ID3 algorithm](/source/ID3_algorithm) for decision tree learning.<ref name="Freitas2013">{{cite book|author=Alex A. Freitas|title=Data Mining and Knowledge Discovery with Evolutionary Algorithms|url=https://books.google.com/books?id=0empCAAAQBAJ|date=11 November 2013|publisher=Springer Science & Business Media|isbn=978-3-662-04923-5}}</ref>{{rp|7}}<ref name=TriantaphyllouFelici2006 />{{rp|348}} Rule learning algorithm are taking training data as input and creating rules by partitioning the table with [cluster analysis](/source/cluster_analysis).<ref name=Freitas2013 />{{rp|7}} A possible alternative over the ID3 algorithm is genetic programming which evolves a program until it fits to the data.<ref name="PappaFreitas2009">{{cite book|author1=Gisele L. Pappa|author2=Alex Freitas|title=Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach|url=https://books.google.com/books?id=nWJHAAAAQBAJ&pg=PA126|date=27 October 2009|publisher=Springer Science & Business Media|isbn=978-3-642-02541-9}}</ref>{{rp|2}}

Creating different algorithm and testing them with input data can be realized in the WEKA software.<ref name=PappaFreitas2009 />{{rp|125}} Additional tools are machine learning libraries for [Python](/source/Python_(programming_language)), like [scikit-learn](/source/scikit-learn).

== Paradigms ==

Some major rule induction paradigms are:
*[Association rule learning](/source/Association_rule_learning) algorithms (e.g., Agrawal)
*[Decision rule](/source/Decision_rules) algorithms (e.g., Quinlan 1987)
*[Hypothesis testing](/source/Hypothesis_testing) algorithms (e.g., RULEX)
*[Horn clause](/source/Horn_clause) induction
*[Version spaces](/source/Version_spaces)
*[Rough set](/source/Rough_set) rules
*[Inductive Logic Programming](/source/Inductive_Logic_Programming)
*Boolean decomposition (Feldman)

== Algorithms ==

Some rule induction algorithms are:
*Charade<ref>Sahami, Mehran. "[https://web.archive.org/web/20170905095851/https://pdfs.semanticscholar.org/9039/68adbb73916120b67d8098e5df95a0166eb6.pdf Learning classification rules using lattices]." Machine learning: ECML-95 (1995): 343-346.</ref>
*Rulex
*[Progol](/source/PROGOL)
*[CN2](/source/CN2_algorithm)

== References ==
<references />
*{{cite conference
  | first = J. R.
  | last = Quinlan
  | title = Generating production rules from decision trees
  | book-title = Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87)
  | pages = 304–307
  | date = 1987
  | location = Milan, Italy
  | editor = McDermott, John
  | url = http://www.ijcai.org/Proceedings/87-1/Papers/063.pdf}}

Category:Machine learning
Category:Inductive reasoning

{{machine-learning-stub}}

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Adapted from the Wikipedia article [Rule induction](https://en.wikipedia.org/wiki/Rule_induction) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Rule_induction?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.