摘要
随着作战实验的发展,作战实验数据呈现出规模海量化、类型多样化、处理快速化等特征,传统实验数据分析方法存在存储和运算能力不足等问题。对海量实验数据关联规则挖掘进行了研究,设计了挖掘框架,明确了挖掘流程,并对现有关联规则挖掘算法进行了改进。最后,通过案例验证了框架和流程的有效性,仿真结果表明,改进后算法的挖掘效率得到有效提升。
Along with the development of the combat experiment,the size of the combat experimental data becomes huge,the type becomes various and the process becomes rapidity,while the traditional experimental data analysis method is short at storage and calculate.This paper does some researches on the application of association rule mining in combat experiment date analysis,builds a data association rule mining framework,defines the mining process and improved the existing mining algorithms.Finally,a case is given to verify the validity of the framework and the process,the simulation results also show that the mining efficiency of the improved algorithm has been increased.
作者
曹冠平
王跃利
张立韬
CAO Guan-ping;WANG Yue-li;ZHANG Li-tao(Academy of Military Science,Beijing 100091,China)
出处
《指挥控制与仿真》
2019年第2期70-74,共5页
Command Control & Simulation
关键词
作战实验数据
数据分析
关联规则
挖掘
算法
combat experimental data
data analysis
association rule
mining
algorithm
