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Spatio-temporal data clustering based on type-2 fuzzy sets and cloud models

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4 Author(s)
Kun Qin ; Sch. of Remote Sensing Inf. Eng., Wuhan Univ., Wuhan, China ; Mengran Wu ; Lingqiao Kong ; Yao Liu

The time series remote sensing data and meteorological satellite data offer new opportunities for understanding the earth system. Spatio-temporal data clustering becomes a kind of idea tool to explore huge data space of spatio-temporal data. Because there are many uncertainties in the huge spatio-temporal data, including fuzziness and randomness, the spatio-temporal clustering methods with uncertainties are needed. Based on type-2 fuzzy sets and cloud models, the paper analyzes the uncertainty of the membership of FCM (fuzzy C-means), and proposes CFFCM (cloud fuzzifier fuzzy C-means) method. Take the time series SST (sea surface temperature) data as examples, the paper applies CFFCM to carry out spatio-temporal clustering analysis, and discovers some interesting patterns.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Date of Conference:

25-30 July 2010