Mining High-temperature Event Space-time Regions in Geo-referenced Temperature Series Data
Mining space-time regions of events is an important task in data mining.It has wide applications in various disciplines,such as epidemiology,meteorology.The existing space-time regions events mining algorithms usually based on clustering analysis,which is difficult to detect irregularly shaped events when they evolve by time.Meanwhile parameter-setting is also a difficult problem for most existing methods.In this paper,we propose a novel automatic event mining algorithm--Gtem.Combined with Minimum Length Description(MDL)principle,Gtem can optimize parameter-setting; detect event regions of different evolutions according to the spatial-temporal correlations of objects and find outliers as well.We conduct experiments on daily-weather datasets of Hunan province from 2004-2008 and the experimental results show that the proposed Gtem could find high-temperature space-time regions efficiently.
geo-referenced time series spatial-temporal clustering analysis spatial-temporal event mining
Xue Bai Yitong Wang Heng Jiang Zhicheng Liao Yun Xiong Xibin Shi
Shanghai Key Laboratory of Data Science,Fudan University,Shanghai,China School of Computer Science,Fudan University,Shanghai,China
国际会议
厦门
英文
681-686
2014-08-19（万方平台首次上网日期，不代表论文的发表时间）