An algorithm is proposed for mining fuzzy association rules based on immune principles , which is mainly inspired by the clonal selection principle of biological immune systems. It is employed to optimize the number of st rong rules that satisfy the specified thresholds by adjusting the parameters of fuzzy sets for each quantitative att ribute. The performances of the algorithm is compared with other relevant algorithms and the experimental result s show the effectiveness of the algorithm.

基于免疫原理的模糊关联规则挖掘算法.pdf

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