Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

• 作者: Cunqun Fan Lan Wei Lizi Xie Manyun Lin Peng Guo Xiangang Zhao
• 来源期刊: 地球科学和环境保护期刊(英文)
• 学科: 工学
• 关键词: Principal COMPONENT ANALYSIS Improved K-Mean ALGORITHM METEOROLOGICAL Data Processing FEATURE ANALYSIS SIMILARITY ALGORITHM
• 年，卷（期）: 2017,（7）
• 页码范围: 39-48
• 页数: 10页
• 分类号: TP39

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.