Mining geographic episode association patterns of abnormal events in global earth science data  

作　　者：WU TianShu,SONG GuoJie,MA XiuJun,XIE KunQing,GAO XiaoPing & JIN XingXing Key Laboratory of Machine Perception,Ministry of Education,and Department of Machine Intelligence,Peking University,Beijing 100871,China 

出　　处：《Science China(Technological Sciences)》2008年第S1期155-164,共10页中国科学（技术科学英文版）

基　　金：the National Hi-Tech Research and Development Program of China (Grand No. 2006AA12Z217);the National Natural Science Foundation of China (Grant No. 60703066)

摘　　要：Abnormal events in earth science have great influence on both the natural envi-ronment and the human society. Finding association patterns among these events has great significance. Because data in earth science has characteristics of mass,high dimension,spatial autocorrelation and time delay,existing mining technolo-gies cannot be directly used on it. We propose a RSNN (range-based searching nearest neighbors) spatial clustering algorithm to reduce the data size and auto-correlation. Based on the clustered data,we propose a GEAM (geographic episode association pattern mining) algorithm which can deal with events time lags and find interesting patterns with specific constraints,to mine the association patterns. We carried out experiments on global climate datasets and found many interesting association patterns. Some of the patterns are coincident with known knowledge in climate science,which indicates the correctness and feasibilities of our methods,and the others are unknown to us before,which will give new information to this research field.Abnormal events in earth science have great influence on both the natural envi-ronment and the human society. Finding association patterns among these events has great significance. Because data in earth science has characteristics of mass,high dimension,spatial autocorrelation and time delay,existing mining technolo-gies cannot be directly used on it. We propose a RSNN (range-based searching nearest neighbors) spatial clustering algorithm to reduce the data size and auto-correlation. Based on the clustered data,we propose a GEAM (geographic episode association pattern mining) algorithm which can deal with events time lags and find interesting patterns with specific constraints,to mine the association patterns. We carried out experiments on global climate datasets and found many interesting association patterns. Some of the patterns are coincident with known knowledge in climate science,which indicates the correctness and feasibilities of our methods,and the others are unknown to us before,which will give new information to this research field.

关 键 词：ABNORMAL EVENTS ASSOCIATION patterns high DIMENSIONAL clustering earth science data 

分 类 号：P208[天文地球—地图制图学与地理信息工程]