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On Genetic-Fuzzy Data-Mining Techniques

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Encyclopedia of Complexity and Systems Science

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Glossary

Data Mining: :

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. The common techniques include mining association rules, mining sequential patterns, clustering, and classification, among others.

Fuzzy Set Theory: :

The fuzzy set theory was first proposed by Zadeh in 1965. It is primarily concerned with quantifying and reasoning using natural language in which words can have ambiguous meanings. It is widely used in a variety of fields because of its simplicity and similarity to human reasoning.

Fuzzy Data Mining: :

The concept of fuzzy sets can be used in data mining to handle quantitative or linguistic data. Basically, fuzzy data mining first uses membership functions to transform each quantitative value into a fuzzy set in linguistic terms and then uses a fuzzy mining process to find fuzzy association rules.

Genetic Algorithms: :

Genetic Algorithms (GAs) were first proposed by Holland in 1975....

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  2. Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan

    Chun-Hao Chen

  3. Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

Authors
  1. Tzung-Pei Hong
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  2. Chun-Hao Chen
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  3. Vincent S. Tseng
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Corresponding author

Correspondence to Tzung-Pei Hong .

Editor information

Editors and Affiliations

  1. Ramtech Ltd., Larkspur, CA, USA

    Robert A. Meyers

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Hong, TP., Chen, CH., Tseng, V.S. (2022). On Genetic-Fuzzy Data-Mining Techniques. In: Meyers, R.A. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_244-2

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  • DOI: https://doi.org/10.1007/978-3-642-27737-5_244-2

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  • Print ISBN: 978-3-642-27737-5

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