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논문검색

A Survey on Clustering based Meteorological Data Mining

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Problem Definition
 3. The Commonly used Clustering Algorithms in Meteorology
  3.1 Partitioning clustering
  3.2 Hierarchical clustering
  3.3 Model-based clustering
  3.4 Fuzzy clustering
  3.5 Combinatorial search techniques-based clustering
 4. The Applications of Clustering Algorithms in Meteorology
  4.1 Climate change
  4.2. Urban meteorology
  4.3 Hydrometeorology
  4.4 Energy meteorology
 5. Discussion
 Acknowledgements
 References

저자정보

  • Wei Tian College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Yuhui Zheng College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Runzhi Yang National Meteorological Information Center, China Meteorological Administration, Beijing 100080, China
  • Sai Ji College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Jin Wang College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, Chin

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