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A soft-computing-based method for the automatic discovery of fuzzy rules in databases: Uses for academic research and management support in marketing

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  • Orriols-Puig, Albert
  • Martínez-López, Francisco J.
  • Casillas, Jorge
  • Lee, Nick

Abstract

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentation of an analytical method based on fuzzy logic and evolutionary algorithms, developed to analyze marketing databases and uncover relationships among variables. A detailed implementation on a pre-existing data set illustrates the method.

Suggested Citation

  • Orriols-Puig, Albert & Martínez-López, Francisco J. & Casillas, Jorge & Lee, Nick, 2013. "A soft-computing-based method for the automatic discovery of fuzzy rules in databases: Uses for academic research and management support in marketing," Journal of Business Research, Elsevier, vol. 66(9), pages 1332-1337.
    • Handle: RePEc:eee:jbrese:v:66:y:2013:i:9:p:1332-1337
    DOI: 10.1016/j.jbusres.2012.02.033
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    References listed on IDEAS

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    Cited by:

    1. Chung-Shing Chan, 2017. "The application of fuzzy sets theory in eco-city classification," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 13(1), pages 4-17, February.
    2. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.

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