一种基于误差和关键点的地震前兆观测数据异常挖掘算法
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摘要
地震前兆观测数据是对地震进行分析和预测的重要依据。但是当前往往是以人工处理为主要手段,面对海量的前兆观测数据,迫切需要切实可行的异常挖掘算法。提出了基于误差和关键点的自顶向下(error andkey-point top-down,EKTW)分段算法以及基于时间邻域的局部异常因子(time-neighbourhood local outlier factor,TLOF)分析方法。相比于传统的分段算法在高分辨率下近似效果不佳、对发现短时高频异常会造成一定程度影响的缺陷,EKTW分段算法通过对时间序列中的关键点的识别和保留进行了弥补和加强。而基于时间邻域的局部异常因子(TLOF)则考虑到了地震前兆观测数据中的时间属性,在异常挖掘中以时间邻域对象作为参考来评价离群程度。实验表明,以上算法对发现地震前兆观测数据中的两类典型异常具有较好的效果。
Seismic precursory observation data is the very important basis for seismic analysis and forecast.However,the artificial methods are the main mode to deal with the huge data.In order to solve this problem,it need a practical abnormity mining algorithm.This paper brought forward a segment algorithm named EKTW and an abnormity analysis method based on local outlier factor of time domain neighbor(TLOF).The conventional segment algorithm had a poor approximate ability under the high resolution,which brought some bad effect in the process of discovering short-time high-frequency abnormity.Compared with the defect of the conventional segment algorithm,EKTW segment algorithm identifies and holds the key points in time series,which enhances the approximate ability under high resolution.Taking the time attribute into account,the index TLOF evaluates the abnormal degree of an object with its time domain neighbors.Experiments show that the algorithms described above have a good effect in finding the two kind of representative abnormity in seismic precursory observation data.
Seismic precursory observation data is the very important basis for seismic analysis and forecast.However,the artificial methods are the main mode to deal with the huge data.In order to solve this problem,it need a practical abnormity mining algorithm.This paper brought forward a segment algorithm named EKTW and an abnormity analysis method based on local outlier factor of time domain neighbor(TLOF).The conventional segment algorithm had a poor approximate ability under the high resolution,which brought some bad effect in the process of discovering short-time high-frequency abnormity.Compared with the defect of the conventional segment algorithm,EKTW segment algorithm identifies and holds the key points in time series,which enhances the approximate ability under high resolution.Taking the time attribute into account,the index TLOF evaluates the abnormal degree of an object with its time domain neighbors.Experiments show that the algorithms described above have a good effect in finding the two kind of representative abnormity in seismic precursory observation data.
引文
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[2]KEOGH E,CHU S,HART D,et al.An online algorithm for segmen-ting time series[C]//Proc of the 1st IEEE International Conference onData Mining.Washington DC:IEEE Computer Society,2001:289-296.
[3]KEOGH E.Fast similarity search in the presence of longitudinalscaling in time series databases[C]//Proc of the 9th IEEE Interna-tional Conference on Tools with Artificial Intelligence.WashingtonDC:IEEE Computer Society,1997:578.
[4]范明,孟小峰.数据挖掘概念与技术[M].北京:机械工业出版社,2007.
[5]KNORR E M,NG R T.Algorithms for mining distance-based outliersin large datasets[C]//Proc of the 24th International Conference onVery Large Data-Bases.San Francisco,CA:Morgon Kaufmann Pub-lishers Inc,1998:392-403.
[6]殷瑞飞.数据挖掘中的聚类方法及其应用[D].厦门:厦门大学,2008.
[7]黄洪宇,林甲祥,陈崇成,等.离群数据挖掘综述[J].计算机应用研究,2006,23(8):8-13.
[8]沙朝锋.基于信息论的数据挖掘算法[D].上海:复旦大学,2008.
[9]任斐.基于数据挖掘的自适应异常检测研究[D].长春:吉林大学,2009.
[10]贾俊杰.空间数据挖掘中若干关键技术研究[D].西安:长安大学,2009.
[11]黄厚宽.数据挖掘可视化模型及其应用研究[D].北京:北京交通大学,2009.
[12]李光强,郑茂仪,邓敏.时空数据异常探测方法[J].计算机工程,2010,36(5):35-37.
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