基于蚁群算法的非结构化大数据深度挖掘仿真

Deep Mining Simulation of Unstructured Big Data Based on Ant Colony Algorithm

针对传统的非结构化大数据深度挖掘方法结果不准确等问题,提出基于蚁群算法的非结构化大数据深度挖掘方法。以蚁群算法的参数选取原则作为标准,分别连接OpenFlow控制器及大数据批量处理模块,完成基于蚁群算法的非结构化挖掘环境建立。利用开源型挖掘框架,定义PPC-Tree型树状组织,再通过计算大数据K均值的方式,实现非结构化大数据深度挖掘算法的顺利应用。实验结果表明,应用所提深度挖掘方法后,节点组织的总容纳承载量大幅提升,匹配路径的平均传输速率也显著提高,能够准确挖掘非结构化大数据。

Due to the inaccuracy of traditional method,a method of deep mining for unstructured big data based on ant colony algorithm was proposed.Based on the principle of parameter selection of ant colony algorithm,the Open Flow controller and batch processing module of big data were connected to establish the unstructured mining environment based on ant colony algorithm.The open source mining framework,PPC-Tree tree organization was defined.Finally,the unstructured big data deep mining algorithm was successfully applied by calculating the K-means of big data.Simulation results show that the total capacity of node organization and the average transmission rate of the matching path are greatly improved after applying the proposed.This method can accurately mine the unstructured big data.