利用PCA与层次聚类算法的军事训练数据分析方法  

Military training data analysis method using PCA and hierarchical clustering algorithm

作　　者：韩曜权 毕增军[1] 李广强[1] 王敏[1] HAN Yaoquan, BI Zengjun, LI Guangqiang, WANG Min(Air Force Early Warning Academy, Wuhan 430019, Chin)

机构地区：[1]空军预警学院,武汉430019

出　　处：《空军预警学院学报》2018年第2期132-136,共5页Journal of Air Force Early Warning Academy

摘　　要：为了更好地科学化、精细化开展军事训练,提出一种对军事训练数据采用数据挖掘手段进行分析的方法.该方法针对军事训练数据量大、科目多、维度高的特点,首先运用主成分分析(PCA)算法进行降维处理,然后基于传统层次聚类算法对训练数据进行分析,最后给出了实验结果.实验结果表明,该方法能有效地根据成绩分布区分训练人员,直观反映出各类训练人员的成绩特点,对军事训练计划制定与实施提供参考.In order to better carry out military training in a scientific and refined manner, this paper puts for- ward a method for analyzing military training data by means of data mining. The proposed method is aimed at the characteristics of large amount of military training data, multi-subjects and high dimensionality, and uses principal component analysis （PCA） algorithm to reduce the dimensions. And then the paper analyzes training data based on the traditional hierarchical clustering algorithm. Finally, the paper offers the experimental results, which show that the proposed method effectively distinguishes the trainees according to the score distribution, and intuitively reflects the performance characteristics of all kinds of trainees, thus providing a reference for formulating and im- plementing military training plans.

关 键 词：军事训练 数据挖掘 主成分分析 层次聚类 

分 类 号：E919[军事] TP391.4[自动化与计算机技术—计算机应用技术]