北京师范大学学报（自然科学版）2017，Vol.53Issue(6)：656-662,7.DOI:10.16360/j.cnki.jbnuns.2017.06.005
基于主成分分析和改进K-means算法的极轨气象卫星数据处理软件分型研究
Data processing software for polar orbit meteorological satellite software classification based on principal component analysis and improved K-means cluster algorithm
摘要
Abstract
We propose here a new method of meteorological software classification based on principal component analysis (PCA) and improved K-means algorithm.The method first references PCA for feature dimensionality reduction and interpretation softwares,then adopts improved K-means algorithm for classification of meteorological data processing software,before PCA results are used to explain software operating characteristics and significance.The classifications of this method are consistent with actual situation,a set of index systems are then used to describe various kinds of software.This index system can be used to optimize allocation of software and hardware resources and to improve efficiency of software operation.关键词
主成分分析/改进K-means算法/特征分析/相似度算法/指标体系Key words
PCA/improved K-means algorithm/feature analysis/similarity algorithm/index system分类
信息技术与安全科学引用本文复制引用
林曼筠,赵现纲,皇甫大鹏,陈平..基于主成分分析和改进K-means算法的极轨气象卫星数据处理软件分型研究[J].北京师范大学学报（自然科学版）,2017,53(6):656-662,7.基金项目
国家863计划资助项目(2011AA12A104) （2011AA12A104）
