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A data mining algorithm for fuzzy transaction data

Author

Listed:
  • Chin-Yuan Chen
  • Gin-Shuh Liang
  • Yuhling Su
  • Mao-Sheng Liao

Abstract

The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers’ fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Chin-Yuan Chen & Gin-Shuh Liang & Yuhling Su & Mao-Sheng Liao, 2014. "A data mining algorithm for fuzzy transaction data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 2963-2971, November.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:2963-2971
    DOI: 10.1007/s11135-013-9934-1
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    Cited by:

    1. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.

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