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WIREs Data Mining Knowl Discov
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On the discovery of association rules by means of evolutionary algorithms

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Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases. A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones. The use of fuzzy rules in the evolutionary algorithms for association rule learning is also described. Finally, the main applications of association rule evolutionary learning covered by the specialized bibliography are reviewed. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 397–415 DOI: 10.1002/widm.18

This article is categorized under:

  • Algorithmic Development > Association Rules
  • Technologies > Association Rules
  • Technologies > Computational Intelligence
Figure 1.

Some examples of individual representation (chromosome = rule) and their corresponding decodified rules.

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Figure 2.

Individual representation (chromosome = rule) used in.25, 26

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Figure 3.

Individual representation (chromosome = rule or itemset).

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Technologies > Computational Intelligence

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