基于免疫克隆文化算法的关联规则挖掘

Mining Association Rules Based on Immune Clone Culture Algorithm

关联规则挖掘是数据挖掘中的一个重要任务,传统关联规则挖掘方法计算复杂度高、效率较低,而智能算法在搜索过程中具有保持种群多样性、鲁棒性等优点。本文提出基于免疫克隆文化算法的关联规则挖掘模型,该模型将免疫克隆算法嵌入到文化算法的框架中,利用免疫克隆算法的全局收敛性在数据库中迅速搜索频繁项目集,进而提取用户感兴趣的关联规则;利用文化算法信念空间的知识结构指导种群的进化,增强了搜索的目的性和方向性。实验表明,该模型具有较快的运行速度,提高了所得关联规则的准确率。

Association rules mining is an important problem in data mining. The traditional mining algorithms have high complexity and low efficiency, while the intelligent algorithms have the advantages of maintenance of population diversity and robustness in the searching process. A model of mining association rules based on immune clone culture algorithm is proposed. This model takes advantages of global searching in the immune clone algorithm to rapidly search the frequent item sets and then extract the interesting rules. It also uses the knowledge structure of belief space in the culture algorithm to guide the population’s evolution and enhance the purpose and directivity of searching. The experiments show that the new model has faster performance speed and also improves the accuracy of the rules.