Sea surface temperature clustering based on type-2 fuzzy theory

Author

Qin, Kun ; Kong, Lingqiao ; Liu, Yao ; Xiao, Qizhi

Author_Institution

Sch. of Remote Sensing Inf. Eng., Wuhan Univ., Wuhan, China

fYear

2010

fDate

18-20 June 2010

Firstpage

1

Lastpage

5

Abstract

Spatial data clustering is an effective method to find interesting spatio-temporal clustering patterns. There are many uncertainties in sea surface temperature (SST) clustering, so clustering methods with uncertainty must be used. Type-2 fuzzy theory takes into account the uncertainty of membership grade while fuzzy C means (FCM) not. Based on the analysis of interval type-2 fuzzy C means (IT2FCM), the paper utilizes two normal cloud models to express fuzzifiers m₁ and m₂, and uses two cloud drops to substitute them. The method considers the uncertainty of two fuzzifiers, and avoids many times of repeated tests, which reduces computation cost. The paper applies the improved IT2FCM into global SST clustering, and discovers some interesting climate patterns.

Keywords

climatology; fuzzy systems; geophysics computing; ocean temperature; pattern clustering; spatiotemporal phenomena; IT2FCM; climate pattern; global SST clustering; interval type-2 fuzzy C means; normal cloud models; sea surface temperature clustering; spatial data clustering; spatiotemporal clustering pattern; type-2 fuzzy theory; uncertainty; Clouds; Clustering methods; Correlation; Meteorology; Ocean temperature; Time series analysis; Uncertainty; SST data; fuzzy clustering; spatial data clustering; type-2 fuzzy theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoinformatics, 2010 18th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-7301-4

Type

conf

DOI

10.1109/GEOINFORMATICS.2010.5567484

Filename

5567484