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基于小波变换的例外挖掘

Wavlet Transform-based Outlier Mining
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摘要 1引言例外常常是指数据集中远远偏离其它对象的那些小比例对象,在大多数研究中作为噪声被遗弃.但是在一些应用中,例外的检测能为我们提供比较重要的信息,使我们发现一些真实而又出乎预料的知识,具有很高的实用价值,如文[1]挖掘了时序数据中的例外,从而在存储容量相同的情况下,可以获得对原始序列更精确的表示. In real project, the useful signal usually shows as low-frequency signal, but noise (outlier) shows as high-frequency signal. Wavlet transform can maps 1-D signal into a 2-D time-scale plane,and the localization of signal is described in different scales,so it is suit to detect instantaneous strange embedded in normal signal. The notion of TS (ω)outlier and CL(δ)outlier,a kind of waylet transform-based cutler mining approach are introduced. Experiments results show its efficiency and effectiveness.
作者 周丽华 王丽珍
机构地区 云南大学信息学院计算机科学与工程系
出处 《计算机科学》 CSCD 北大核心 2002年第2期127-129,共3页 Computer Science
基金 云南省自然科学基金(1999F0015M)
关键词 数据库 数据挖掘 时序数据 小波变换 例外挖掘 Wavlet transform,Outlier mining,Time series outlier,Cluster-based outlier
分类号 TP311.13 [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7

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二级参考文献9

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计算机科学
2002年 第2期

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