基于经典算法的大数据网络流量资源挖掘模型构建

Construction of Big Data Network Traffic Resource Mining Model Based on Classical Algorithm

针对大数据网络流量资源在挖掘过程中经常出现资源分配失衡和利用率低的问题,为了提高大数据网络流量资源的挖掘性能,提出了基于经典算法的大数据网络流量资源挖掘模型构建。基于对大型数据网络流量资源的多维聚类分析,建立了聚类分析的数学模型,并将其映射为一个多维协作的变量向量箱问题,将经典算法引入到网络流量资源挖掘中,并对大数据网络流量资源进行布局,计算出挖掘过程中最大挖掘权限的全局最优解,并结合相似性探索结果,匹配典型算法中的最大挖掘权限,选择经典算法中的欧式距离,定义大数据网络流量资源,根据赋值全图法对大数据网络流量资源进行聚类,利用经典算法模糊聚类,提取网络流量资源特征作为训练集,经过数据迭代后实现大数据网络流量资源挖掘。实验结果表明,实验结果表明,所提模型物理机数量最多为441个,大数据网络流量资源利用率最高为85%,获取新资源的平均操作时间约为10 s,有较明显的优势,充分证明该模型能有效提高大数据网络流量资源的利用率。

In order to improve the mining performance of big data network traffic resources,a mining model of big data network traffic resources based on classical algorithm is proposed.Based on the multi-dimensional clustering analysis of large data network traffic resources,the mathematical model of clustering analysis is established,and it is mapped into a multi-dimensional collaborative variable vector box problem.The classic algorithm is introduced into the network traffic resources mining,and the big data network traffic resources are arranged,and the global optimal solution of the maximum mining authority in the mining process is calculated,and combined with the similarity The exploration results match the maximum mining authority in the typical algorithm,select the Euclidean distance in the classic algorithm,define the big data network traffic resources,cluster the big data network traffic resources according to the assignment full graph method,use the classic algorithm fuzzy clustering,extract the network traffic resources characteristics as the training set,and realize the big data network traffic resources mining after data iteration.The experimental results show that the number of physical machines in the proposed model is 441 at most,the utilization rate of big data network traffic resources is 85%at most,and the average operation time to obtain new resources is about 10 s,which has obvious advantages.It fully proves that the model can effectively improve the utilization rate of big data network traffic resources.