Comparative Analysis of Mining Fuzzy Association Rule using Genetic Algorithm

Genetic algorithm is a global optimization search technique that offers a powerful search method. Fuzzy logic is used to find association rules that can overcome the problems of crisp sets. In this paper, comparison between two genetic fuzzy algorithms: Genetic Cooperative-Competitive Learning Algorithm (GFS.GCCL) and Structural learning algorithm on vague environment (SLAVE) are discussed. Genetic algorithm based machine learning approach named GFS.GCCL, which generates fuzzy rules for classifying patterns. Slave algorithm uses iterative approach to learn fuzzy association rules. This experiment has been performed on two real-world renowned datasets: Iris dataset and Wine dataset. It has found SLAVE algorithm provides a better result than GFS.GCCL algorithm for real world datasets.