A data mining algorithm for fuzzy transaction data

Chin-Yuan Chen (), Gin-Shuh Liang (), Yuhling Su () and Mao-Sheng Liao ()

Quality & Quantity: International Journal of Methodology, 2014, vol. 48, issue 6, 2963-2971

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

Keywords: Trapezoidal fuzzy numbers; Fuzzy similarity; Data mining; Association rule (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11135-013-9934-1

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