基于PSO的云计算环境中大数据优化聚类算法

Big Data Optimization Clustering Algorithm Based on PSO in Cloud Computing Environment

在云计算环境下,对大数据进行优化聚类是实现数据优化访问和挖掘的基础。传统方法采用模糊C均值聚类算法进行云计算中的大数据聚类,易陷入局部极值,产生聚类偏移,效果不佳。提出一种基于优化粒子群(PSO)算法的大数据聚类算法。分析了云计算环境中的大数据结构模型,计算大数据的离散样本频谱特征,实现聚类样本的特征提取和信息模型构建。由于粒子群在搜索过程中经常会陷入局部最优解,采用混沌映射方法,带领粒子逃离局部最优解,设计粒子群优化算法进行特征聚类,达到大数据优化聚类的目的。仿真结果表明,采用该算法进行数据聚类,误分率降低,寻优性能较好,具有较好的应用价值。

In the cloud computing environment,the optimization of big data is the basis for the data optimized access and mining. In the traditional method, the fuzzy C means clustering algorithm is used to cluster the big data in the cloud computing, which is easy to fall into local extremum. A big data clustering algorithm based on Particle Swarm Optimization （PSO） is proposed. The big data structure model in cloud computing environment is analyzed, and the diserete sample spectrum characteristics of big data are calculated, realizing feature extraction and information model construction of clustering sample. The particles are often fallen into local extremum in searching. The chaotic mapping is used to take the particles against the local extremum. The PSO is designed to carry on the feature clustering for the purpose of optimization clustering for big data. Simulation shows that the proposed algorithm is used for data clustering,and the error rate is reduced,and the optimization performance is better, and it has good application value,