基于文化免疫克隆算法的关联规则挖掘研究

针对关联规则挖掘问题，给出一种基于文化免疫克隆算法的关联规则挖掘方法，该方法将免疫克隆算法嵌入到文化算法的框架中，采用双层进化机制，利用免疫克隆算法的智能搜索能力和文化算法信念空间形成的公共认知信念的引导挖掘规则。该方法重新给出了文化算法中状况知识和历史知识的描述，设计了一种变异算子，能够自适应调节变异尺度，提高免疫克隆算法全局搜索能力。实验表明，该算法的运行速度和所得关联规则的准确率优于免疫克隆算法。

Mining association rules based on cultured immune clone algorithm

For the association rules mining, a method of mining association rules based on cultured immune clone algorithm is proposed. This method uses two-layer evolutionary mechanism and embeds the immune clone algorithm in the culture algorithm framework. It uses the intelligent searching ability of the immune clone algorithm and the commonly accepted knowledge in the culture algorithm to guide the rules mining. The situational knowledge and history knowledge in the culture algorithm are redefined, and a new mutation operator is put forward. This operator has the adaptive adjustment of mutation measure to improve the global search ability of immune clone algorithm. The experiments show that the new algorithm is superior to immune clone algorithm in performance speed and the rules’ accuracy.