A Survey on Clustering based Meteorological Data Mining

Data mining is an important tool in meteorological problems solved. Cluster analysis techniques in data mining play an important role in the study of meteorological applications. The research progress of the clustering algorithms in meteorology in recent years is summarized in this paper. First, we give a brief introduction of the principles and characteristics of the clustering algorithms that are commonly used in meteorology. On the other hand, the applications of clustering algorithms in meteorology are analyzed, and the relationship between the various clustering algorithms and meteorological applications are summarized. Then we interpret the relationship from the perspectives of algorithms’ characteristics and practical applications. Finally, some main research issues and directions of the clustering algorithms in meteorological applications are pointed out.

[1]  Josef Ryšavý Studia geophysica et geodaetica , 1957 .

[2]  C. Q. Lee,et al.  The Computer Journal , 1958, Nature.

[3]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[4]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[5]  G. Stout,et al.  Atmospheric research. , 1973, Science.

[6]  中川 信矢 レ-ダ雨量測定法による貯水池流入量予測の精度(Water Resources Research 12-2,′76) , 1976 .

[7]  Proceedings of the IEEE , 2018, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[8]  Tatyana Yakhno,et al.  Advances in Information Systems , 2002, Lecture Notes in Computer Science.

[9]  Justus Notholt,et al.  Journal of Geophysical Research. Atmospheres , 2002 .

[10]  Budapesti Corvinus Egyetem Applied Ecology and Environmental Research , 2003 .

[11]  David Tomko,et al.  The New York Academy of Sciences , 1881, Cellular and Molecular Neurobiology.

[12]  Ge Sun,et al.  Journal of the American Water Resources Association a C O W M I S O N of Six P O T E M W Evmotwspiration Mthods for Regional Use in the Soutmastern M T E D Stams1 , 2005 .

[13]  Guido Cervone,et al.  Risk Assessment of Atmospheric Hazard Releases Using K-Means Clustering , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[14]  A Calculated Methodology of Regional Contributions Based on MM5-CAMx in Typical City: A 2006 Case Study of SO2 and Sulfate , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[15]  Ning Ma,et al.  SAR Water Image Segmentation Based on GLCM and Wavelet Textures , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[16]  Wenjia Wang,et al.  Adaptive K-Means for Clustering Air Mass Trajectories , 2011, IDEAL.

[17]  H. Grassl,et al.  Theoretical and Applied Climatology , 2011 .

[18]  M. Kvakić,et al.  Modeling the Impacts of a Man-Made Lake on the Meteorological Conditions of the Surrounding Areas , 2014 .

[19]  Michael J. Watts,et al.  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Franziska Frankfurter,et al.  The Atmospheric Environment , 2016 .