International Journal of Computer Science & Engineering Technology (IJCSET)
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary valued 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 for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions.