摘要
针对数据关联规则挖掘的不足,提出了一种基于免疫记忆克隆算法的关联规则挖掘方法。算法利用了免疫记忆特性,把挖掘的关联规则存入记忆库,加快了挖掘速度。在克隆扩增过程中,设计了一种基于矢量距的抗体浓度计算方法,保证克隆扩增过程中解的多样性。仿真实验结果表明,现算法具有较快的运行速度,提高了所得关联规则的准确性。
For the shortcomings of data association rules mining, a new association rule mining method based on immune memory clonal algorithm is proposed. The algorithm takes advantage of the immunological memory char- acteristics and mining association rules are stored in the memory, which speed up the mining speed. In the process of clonal expansion, an antibody concentration calculation method based on the vector is designed to ensure the di- versity of solutions in the process of clonal expansion. The simulation results show that the algorithm has a faster running speed and improve the accuracy of the obtained association rules.
出处
《科学技术与工程》
北大核心
2012年第35期9537-9539,9551,共4页
Science Technology and Engineering
关键词
免疫记忆克隆
关联规则
数据挖掘
抗体浓度
immune memory clonal association rule data mining antibody concentration
