时空聚类分析的普适性方法

时空聚类分析是时空数据挖掘领域近年来研究的热点问题,对于揭示时空要素的发展变化趋势、规律以及本质特征具有重要意义.目前,时空聚类分析的研究仍还初步,缺乏具有普适性的时空聚类分析方法.为此,本文首先建立了一套时空聚类分析的普适性理论方法框架.进而,借助时空统计学、智能计算等工具,提出了一种时空一体化的时空聚类方法.该方法很好地顾及了时空数据的时空耦合、时空相关与时空异质特征,避免了过多人为主观因素的干扰,时空聚类结果具有较好的可靠性.通过采用中国陆地区域42年(1951～1992)年平均气温时空数据进行分析,验证了本文提出的理论与方法的可行性与有效性.

A general method of spatio-temporal clustering analysis

Spatio-temporal clustering has been a hot topic in the field 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 spatio-temporal autocorrelations and heterogeneities among spatio-temporal entities,and the coupling in space and time has not been well highlighted.In this paper,a unified 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 finding spatio-temporal cluster in China’s annual temperature database for the period 1951～1992.