提出一种基于免疫原理的人工免疫算法,用于模糊关联规则的挖掘. 该算法通过借鉴生物免疫系统中的克隆选择原理来实施优化操作,它直接从给出的数据中,通过优化机制自动确定每个属性对应的模糊集合,使推导出的满足条件的模糊关联规则数目最多. 将实际数据集和相关算法进行性能比较,实验结果表明了所提出算法的有效性-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.