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小波变换的离群时序数据挖掘分析 被引量：7

Analysis of Time Series Outlier Mining Based on Wavelet Transform
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摘要 针对时序数据进行离群数据挖掘方法的研究。通过对时序数据进行离散小波变换,将其从时域空间变换到频域空间,使时序数据映射为多维空间的点。该方法具有多尺度、时移不变性等特点,经离群时间序列进行离散小波变换后,不仅具有良好的保距性又达到降低维数目的。然后提出一种基于距离的离群时序数据挖掘算法。仿真试验表明了该方法的有效性。 In this paper, the outlier mining method for time series data is investigated. DWT is used to transform the time series data from time domain to frequency domain. The time series data can be mapped into the multidimensional points in multidimensional space. We proposed a distanced-based algorithm to mine the outliers. The simulation results show the effectiveness of the method.
作者 文琪 彭宏
机构地区 西华大学科技处 西华大学计算机与数理学院
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2005年第4期556-558,共3页 Journal of University of Electronic Science and Technology of China
关键词 小波变换 时序数据 离群数据 数据挖掘 wavelet transform time series outlier data data mining
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献6

  • 1Fayyad U, Piatetsky S G, Smyth P. From data mining to knowledge discovery: An overview[A]. Advances in Knowledge Discovery and Data Mining [C]. USA:AAAI/MIT Press, 1996.
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  • 6郑斌祥,杜秀华,席裕庚.时序数据相似性挖掘算法研究[J].信息与控制,2002,31(3):264-267. 被引量：5

二级参考文献7

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共引文献4

同被引文献46

引证文献7

二级引证文献40

电子科技大学学报
2005年 第4期

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