基于改进K均值聚类的战术导弹试验数据分析

Analysis of Tactical Missile Test Data Based on Improved K-Means Clustering

针对战术导弹试验数据分析过程中异常数据筛查困难的问题,提出了一种基于改进K均值聚类的异常试验数据筛查算法。通过选择最优初始簇中心并剔除异常数据,在检测准确度和效率上对K均值聚类算法加以改进。实例分析表明,在保证一定准确率的前提下,改进K均值聚类算法能以较高效率完成试验数据的筛查,达到在一定程度上辅助导弹试验数据分析的目的。

Aiming at the difficulty of screening abnormal data while analyzing the test data of tactical missile,this paper proposes a novel test data analysis algorithm based on improved K-means clustering.By choosing the optimal initial cluster center and rejecting abnormal data,K-means clustering method is improved in respect of accuracy and efficiency.The case study indicates that the proposed test data analysis algorithm can complete screening of test data with high efficiency while ensuring certain accuracy,and can assist tactical missile data analysis to a certain extent.