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Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable fordoi:10.1109/tevc.2007.900992 fatcat:apl7s2ebgzfcrddxa6slaadaay