基于云计算平台的仿生优化聚类数据挖掘算法

Bionic Optimized Clustering Data Mining Algorithm Based on Cloud Computing Platform

为了提高云计算平台数据挖掘的有效性以及数据聚类的性能,采用仿生优化算法与相似聚类相结合的方法来实现云计算平台数据聚类。在相似聚类的优化函数求解过程中,采用狼群优化算法,以头狼的位置来确定聚类中心点,从而实现类别中心点的优化与更新。文中分别采用PBM和DB聚类效果评价方法来对聚类效果进行检验,在满足预设评价标准的情况下,不断进行狼群优化和相似聚类计算,直到达到聚类指标要求为止。经过实验证明,相比一般聚类算法,狼群优化的聚类算法对数据量大且数据维度高的云计算平台数据聚类效果更好,收敛速度更快。

In order to improve the validity of cloud computing platform data mining and the performance of data clustering,this paper combined bionic optimization algorithm with similar clustering to achieve cloud computing platform data clustering.In the process of solving the optimization function of similar clustering,wolf swarm optimization algorithm is used to locate the head wolf position to determine the cluster centers,so as to optimize and update the category centers.PBM and DB clustering effect evaluation methods were used to test the clustering effect,and wolf swarm optimization and similar clustering calculation were carried out continuously until the requirements of clustering index are met.Experiments results show that,compared with general clustering algorithms,wolf swarm optimization clustering algorithm has better clustering effect and faster convergence speed for cloud computing platform with large data volume and high data dimension.