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Rule induction

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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.

Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures.

(2006). 9780387342962, Springer Science & Business Media. . ISBN 9780387342962

In the easiest case, a rule is expressed with “if-then statements” and was created with the ID3 algorithm for decision tree learning.

Alex A. Freitas (2013). 9783662049235, Springer Science & Business Media. . ISBN 9783662049235

Rule learning algorithm are taking training data as input and creating rules by partitioning the table with cluster analysis. A possible alternative over the ID3 algorithm is genetic programming which evolves a program until it fits to the data.

(2009). 9783642025419, Springer Science & Business Media. . ISBN 9783642025419

Creating different algorithm and testing them with input data can be realized in the WEKA software. Additional tools are machine learning libraries for Python, like scikit-learn.

Paradigms

Some major rule induction paradigms are:

Association rule learning algorithms (e.g., Agrawal)
Decision rules 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:

CharadeSahami, Mehran. " target="_blank" rel="nofollow"> Learning classification rules using lattices." Machine learning: ECML-95 (1995): 343-346.
Rulex
PROGOL
CN2