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  • 标题:Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm
  • 本地全文:下载
  • 作者:Manyun Lin ; Xiangang Zhao ; Cunqun Fan
  • 期刊名称:Journal of Geoscience and Environment Protection
  • 印刷版ISSN:2327-4336
  • 电子版ISSN:2327-4344
  • 出版年度:2017
  • 卷号:5
  • 期号:7
  • 页码:39-48
  • DOI:10.4236/gep.2017.57005
  • 语种:English
  • 出版社:Scientific Research Pub
  • 摘要:With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.
  • 关键词:Principal Component AnalysisImproved K-Mean AlgorithmMeteorological Data ProcessingFeature AnalysisSimilarity Algorithm
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