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An Algorithm for Mining Fuzzy Association Rules Based on Immune Principles

In this paper, an algorithm was proposed for mining fuzzy association rules based on natural immune principles. The proposed algorithm is mainly inspired by the clonal selection principle of biological immune systems. It was employed to optimize the number of fuzzy association rules that satisfy the specified thresholds by adjusting the parameters of fuzzy sets for each quantitative attribute. The performance of our algorithm has been compared with other relevant algorithms and the experimental results showed the effectiveness of our algorithm.