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Spatio-temporal clustering has been a hot topic in the feld of spatio-temporal data mining and knowledge discovery.It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world.However,existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities,and the coupling in space and time has not been well highlighted.In this paper,a unifed framework for the clustering analysis of spatio-temporal data is proposed,and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology.Our method is applied successfully to fnding spatio-temporal cluster in China’s annual temperature database for the period 1951 1992.
Spatio-temporal clustering has been a hot topic in the feld of spatio-temporal data mining and knowledge discovery. It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world. Yet, existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities, and the coupling in space and time has not been well well. In this paper, a unifed framework for the clustering analysis of spatio-temporal data is proposed, and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology. Our method is applied successfully to fnd spatio-temporal cluster in China’s annual temperature database for the period 1951 1992.