Using the master-slave parallel architecture for genetic-fuzzy data mining

Data mining is most commonly used in attempts to induce association rules from transaction data. In the past, we used the fuzzy and GA concepts to discover both useful fuzzy association rules and suitable membership functions from quantitative values. The evaluation for fitness values was, however, quite time-consuming. In this paper, we thus propose a parallel genetic-fuzzy mining algorithm based on the master-slave architecture to extract both association rules and membership functions from quantitative transactions. The master processor uses a single population as a simple genetic algorithm does, and distributes the tasks of fitness evaluation to slave processors. The evolutionary processes, such as crossover formed by the master mutation and production are per processor. Both the theoretic analysis and the experimental results show that the speed-up of the proposed parallel algorithm can increase nearly linear along with the number of individuals to be evaluated.