云环境下基于群智能算法的大数据聚类挖掘技术

Big data clustering mining technology based on swarm intelligence algorithm in cloud environment

传统的大数据聚类挖掘技术由于迭代次数过多,使其并行效率下降,为此,设计云环境下基于群智能算法的大数据聚类挖掘技术。在云环境下采用群智能算法初始化聚类中心,计算数据密度参数及类间距离,根据计算结果更新聚类中心,输出距离最小的最优解即为最优划分聚类,设计并行化聚类挖掘,以输出的最优解为依据,完成大数据聚类挖掘。实验结果表明,在数据集相同的情况下,与传统的两种聚类挖掘算法相比,文中设计的云环境下的群智能算法的大数据聚类挖掘算法随着迭代次数的增加,依然保持较高的并行效率,没有出现下降的趋势,说明该算法适合应用在实际项目中。

The traditional big data clustering mining technology has too many iterations,which makes its parallel efficiency decreased.Therefore,the big data clustering mining technology based on swarm intelligence algorithm in cloud environment is designed.In the cloud environment,swarm intelligence algorithm is adopted to initialize the clustering center,calculate data density parameters and distance between classes,update the clustering center according to the calculated results,and output the optimal solution with the smallest output distance as the optimal partitional clustering.The parallel clustering mining is designed,and the big data clustering mining is completed based on the optimal output solution.The experimental results show that,under the condition of the same data set,in comparison with two kinds of traditional clustering mining algorithms,the designed big data clustering mining algorithm based on swarm intelligence algorithm in cloud environment can still keep higher parallel efficiency and there is no downward trend even if the iterations are increasing.The algorithm is suitable for application in actual project.