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has been cited by the following article:

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

  AUTHORS: Manyun Lin, Xiangang Zhao, Cunqun Fan, Lizi Xie, Lan Wei, Peng Guo

  KEYWORDS: Principal Component Analysis, Improved K-Mean Algorithm, Meteorological Data Processing, Feature Analysis, Similarity Algorithm

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.5 No.7, July 13, 2017

  ABSTRACT: 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.