Using divide-and-conquer GA strategy in fuzzy data mining

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This work thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A GA-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated using the fuzzy-supports of the linguistic terms in the large 1-itemsets and the suitability of the derived membership functions. The proposed framework thus maintains multiple populations of membership functions, with one population for one item´s membership functions. The final best set of membership functions gathered from all the populations is used to effectively mine fuzzy association rules.