基于主成分聚类的军品防护包装等级分类

朱霞, 伍岳, 陈俊斌, 武小琴, 赵萍, 谭德宏, 王凯俊, 赵湖钧

包装工程(技术栏目) ›› 2017 ›› Issue (23) : 21-25.

包装工程(技术栏目) ›› 2017 ›› Issue (23) : 21-25.

基于主成分聚类的军品防护包装等级分类

  • 朱霞1, 赵萍1, 伍岳2, 陈俊斌2, 武小琴2, 谭德宏2, 王凯俊2, 赵湖钧2
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Classification of Military Protective Packaging Level Based on Principal Component Clustering

  • ZHU Xia1, ZHAO Ping1, WU Yue2, CHEN Jun-bin2, WU Xiao-qin2, TAN De-hong2, WANG Kai-jun2, ZHAO Hu-jun2
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摘要

目的 分析不同军品防护包装需求的差异性,根据防护包装需求差异研究军品防护包装等级分类问题。方法 通过采用主成分分析法对选取的军品防护包装等级分类指标进行降维处理,再将各种军品按照降维精简得到的新指标进行聚类分析。结果 通过指标降维处理得到了新的3个分类指标和各种军品关于新指标的得分值,并以此将10种军品按照不同防护需求分为3个军品防护包装等级类别。结论 该方法剔除了分类指标间的信息重叠,区分了不同军品的防护包装需求差异,并根据需求差异对各种军品进行了防护包装等级分类。

Abstract

The work aims to analyze the difference of various military protective packaging requirements, and study the classification of military protective packaging levels according to the demand difference of protective packaging. The classification indexes of protective packaging levels for the selected military products were subject to the dimension reduction process through the principal component analysis method, and then the clustering analysis was conducted based on the new indexes obtained from the dimension reduction of various military products. Three new classification indexes and scores for the new indexes of a variety of military products were obtained by index dimension reduction. Based on that, ten kinds of military products were divided into three military protective packaging levels according to the different protection requirements. This method eliminates the information overlap between classification indexes, distinguishes the difference of protective packaging demand for different military products and classifies the protective packaging levels according to the demand difference of all kinds of military products.

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朱霞, 伍岳, 陈俊斌, 武小琴, 赵萍, 谭德宏, 王凯俊, 赵湖钧. 基于主成分聚类的军品防护包装等级分类[J]. 包装工程(技术栏目). 2017(23): 21-25
ZHU Xia, WU Yue, CHEN Jun-bin, WU Xiao-qin, ZHAO Ping, TAN De-hong, WANG Kai-jun, ZHAO Hu-jun. Classification of Military Protective Packaging Level Based on Principal Component Clustering[J]. Packaging Engineering. 2017(23): 21-25

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