Back
 GEP  Vol.5 No.7 , July 2017
Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm
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.
Cite this paper: Lin, M. , Zhao, X. , Fan, C. , Xie, L. , Wei, L. and Guo, P. (2017) Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm. Journal of Geoscience and Environment Protection, 5, 39-48. doi: 10.4236/gep.2017.57005.
References

[1]   Chen, Z. and Luo, C.C. (2015) Application of an Improved K-Means Algorithm in Anomaly Detection. Journal of Chongqing University of Technology: Natural Science, No. 5, 66-70.

[2]   Fang, C., Yang, Y. and Wu, S.J. (2009) Application of Principal Component Analysis and Cluster Analysis in Software Reconstruction. Computer Engineering and Design, 30, 365-369.

[3]   Li, Z.-Y., Ding, J. and Peng, L.-H. (2004) Principles and Methods of Environmental Quality Assessment. Chemical Industry Press, Beijing.

[4]   Jia, R.-Y. and Song, J.-L. (2016) K-Means Optimal Cluster Number Determination Method Based on Clustering Center Optimization. Microelectronics & Computer, 33, 62-66.

[5]   Yin, C.-X., Zhang, H.-J., Zhang, R., Qi, X.-L. and Wang, B. (2014) An Improved K-Means Algorithm. Computer Technology and Development, 24, 30-33.

[6]   Li, Y.-S., Yang, S.-L. and Ma, X.-J. (2006) Study on K-Value Optimization in Spatial Clustering Algorithm. Journal of System Simulation, 18, 573-576.

[7]   Mac Queen, J. (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.

[8]   Huang, F., Da, P., Lin, Q.H., Zhou, J.M., et al. (2009) An Improved Similarity Algorithm for Personalized Recommendation. International Forum on Computer Science-Technology and Applications, 25-27 December 2009, 54-57.
https://doi.org/10.1109/ifcsta.2009.20

[9]   Abraham, M.H., Grellier, P.L., Prior, D.V., et al. (1990) Hydrogen Bonding. Part 10. A Scale of Solute Hydrogen-Bond Basicity Using Log K Values for Complexation in Tetrachloromethane. Journal of the Chemical Society, Perkin Transactions, 2, 521-529.
https://doi.org/10.1039/p29900000521

[10]   Soylev, T.A. (2016) Comparison of Measured and Prescribed K-Values for the Equivalent Performance of Fly Ash Concrete. Service Life of Cement-Based Materials and Structures, 187.

 
 
Top