Meteorological Data Analyze Base on K-means Algorithm

Author

Jinghua, Huang ; Zhenchong, Wang ; Mei, Yuan ; Youwen, Bao

Author_Institution

Sch. of Mech., Electron. & Inf. Eng., China Univ. of Min. & Technol., Beijing, China

Volume

2

fYear

2009

fDate

12-14 Dec. 2009

Firstpage

60

Lastpage

63

Abstract

The paper proposed a clustering method of decade observation data based on k-means algorithm, which adjusted the weight influence to similarity function by the missing values handling and scaling of range fields. This paper discussed the way to select initial cluster centers and the process of calculating cluster centers and assigning records to clusters. The test indicated the k-means algorithm had effective clustering result.

Keywords

data analysis; meteorology; statistical analysis; cluster centers; decade observation data; k-means algorithm; meteorological data analysis; missing values handling; range fields scaling; similarity function; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data analysis; Euclidean distance; Iterative algorithms; Machine learning algorithms; Meteorology; Partitioning algorithms; Weather forecasting; clustering; k-means algorithm; meteorological data;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on

Conference_Location

Changsha

Print_ISBN

978-0-7695-3865-5

Type

conf

DOI

10.1109/ISCID.2009.164

Filename

5368948